The Draper Technology Digest - Draper Laboratory

The Draper Technology Digest
2011 Volume 15 | CSDL-R-3028

Front cover photo:
(from left to right) Troy B. Jones, Autonomous Systems Capability Leader; Nirmal Keshava,
Group Leader for Fusion, Exploitation, and Inference Technologies; Sungyung Kim, Senior
Member of the Technical Staff in Strategic and Space Guidance and Control; and Amy E.
Duwel, Group Leader for RF and Communications

The Draper Technology Digest (CSDL-R-3028) is published annually
under the auspices of The Charles Stark Draper Laboratory, Inc., 555
Technology Square, Cambridge, MA 02139. Requests for individual
copies or permission to reprint the text should be submitted to:
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Fax: (617) 258-1800
email: techdigest@draper.com
Editor-in-Chief
Michael J. Matranga
Artistic Director
Pamela Toomey
Designer
Lindsey Ruane
Editor
Beverly Tuzzalino
Writers
Jeremy Singer
Amy Schwenker
Alicia Prewett
Illustrator
William Travis
Photographer
James Thomas
Photography Coordinator
Drew Crete
Copyright © 2011 by The Charles Stark Draper Laboratory, Inc.
All rights reserved.

Table of Contents
4
6
9
21
33
47
55
71
83
2
Introduction
by Dr. John Dowdle, Vice President of Engineering
2011 Charles Stark Draper Prize
PAPERS
Oscillator Phase Noise: Systematic Construction of an Analytical Model Encompassing Nonlinearity
Paul A. Ward and Amy E. Duwel
In Vitro Generation of Mechanically Functional Cartilage Grafts Based on Adult Human Stem Cells and
3D-Woven poly(ε-caprolactone) Scaffolds
Piia K. Valonen, Franklin T. Moutos, Akihiko Kusanagi, Matteo G. Moretti, Brian O. Diekman, Jean F. Welter,
Arnold I. Caplan, Farshid Guilak, and Lisa E. Freed
General Bang-Bang Control Method for Lorenz Augmented Orbits
Brett J. Streetman and Mason A. Peck
Tactical Geospatial Intelligence from Full Motion Video
Richard W. Madison and Yuetian Xu
Model-Based Design and Implementation of Pointing and Tracking Systems: From Model to Code in One Step
Sungyung Lim, Benjamin F. Lane, Bradley A. Moran, Timothy C. Henderson, and Frank A. Geisel
Detection of Deception in Structured Interviews Using Sensors and Algorithms
Meredith G. Cunha, Alissa C. Clarke, Jennifer Z. Martin, Jason R. Beauregard, Andrea K. Webb, Asher A.
Hensley, Nirmal Keshava, and Daniel J. Martin
Requirements-Driven Autonomous System Test Design: Building Trusting Relationships
Troy B. Jones and Mitch G. Leammukda
Dithering of 5-arcsec LOS change
Table of Contents

92
101
102
105
106
107
108
109
110
111
112
114
116
List of 2010 Published Papers and Presentations
PATEnTS
Patents Introduction
Systems and Methods for High Density Multi-Component Modules
U.S. Patent No. 7,727,806; Date Issued: June 1, 2010
Scott A. Uhland, Seth M. Davis, Stanley R. Shanfield, Douglas W. White, and Livia M. Racz
List of 2010 Patents
AwARDS
The 2010 Draper Distinguished Performance Awards
Design and Demonstration of a Guided Bullet for Extreme Precision Engagement of Targets at Long Range
Laurent G. Duchesne, Richard D. Elliott, Robert M. Filipek, Sean George, Daniel I. Harjes,
Anthony S. Kourepenis, and Justin E. Vican
Development of an Ultra-Miniaturized, Paper-Thin Power Source
Stanley R. Shanfield, Albert C. Imhoff, Thomas A. Langdo, Balasubrahmanyan “Biga” Ganesh,
and Peter A. Chiacchi
The 2010 Outstanding Task Leader Awards
COTS Guidance, navigation, and Targeting
Ian T. Mitchell
MK6 System Test Complex
Daniel J. Monopoli
The 2010 Howard Musoff Student Mentoring Award
Sarah L. Tao
The 2010 Excellence in Innovation Award
navigation by Pressure
Catherine L. Slesnick, Benjamin F. Lane, Donald E. Gustafson, and Brad D. Gaynor
List of 2010 Graduate Research Theses
Table of Contents
3

Introduction by Dr. John Dowdle, Vice President of Engineering
Introduction by
This publication, the 15th issue of the Draper Technology
Digest, presents a collection of publications, patents,
and awards representative of the outstanding technical
achievements by Draper staff members. Seven technical
papers are presented in this issue to showcase work
associated with Draper’s capabilities and technologies.
These publications represent long-standing Draper core
capabilities in the areas of guidance, navigation, and
control, autonomous systems and information systems,
as well as emerging strengths in biomedical systems and
multimodal sensor fusion.
This issue also recognizes the winners of several
Draper awards for technical excellence, leadership,
and mentoring. The Distinguished Performance Award
is the most prestigious technical achievement award
that the Laboratory bestows upon its employees. This
year's award was presented to two teams. Laurent
Duchesne, Richard Elliott, Robert Filipek, Sean George,
Daniel Harjes, Anthony Kourepenis, and Justin Vican
were acknowledged for “Design and Demonstration
of a Guided Bullet for Extreme Precision Engagement
of Targets at Long Range,” work that resulted in the
development of a guidance system for a 50-caliber
bullet. Stanley Shanfield, Albert Imhoff, Thomas
Langdo, Balasubrahmanyan “Biga” Ganesh, and Peter
Chiacchi were recognized for “Development of an
Ultra-Miniaturized, Paper-Thin Power Source,” which
represents a dramatic breakthrough in miniature
portable energy.
Exceptional technical efforts require outstanding
leadership. Two individuals were awarded the
Outstanding Task Leader Award this year: Ian Mitchell
was recognized for his leadership of “COTS Guidance,
Navigation, and Targeting,” while Daniel Monopoli
was acknowledged for directing the “MK6 System Test
Complex.” Student leadership and mentoring remain

Dr. John Dowdle,
Vice President of Engineering
a priority at Draper. The Howard Musoff Student Mentoring Award
recognizes exceptional student mentoring while simultaneously
honoring a former Draper mentor who devoted much time and energy
to many students. Sarah Tao was the sixth recipient of the Howard
Musoff Student Mentoring Award.
Innovation is a key element to Draper’s success. Two awards
acknowledge innovation of our technical staff. The 2010 Excellence
in Innovation Award recognized a team effort by Catherine Slesnick,
Benjamin Lane, Donald Gustafson, and Brad Gaynor for “Navigation
by Pressure.” The second team recognized for innovation was Seth
Davis, Stanley Shanfield, Douglas White, and Livia Racz, who received
the 2010 Vice President’s Best Patent Award for “Systems and Methods
for High Density Multi-Component Modules.”
The Vice President’s Best Paper Award recognizes an original publication
that represents Draper’s high standards of professionalism,
originality, and creativity. This year’s recipients of the Best Paper
Award were Paul Ward and Amy Duwel, who authored the paper “Oscillator
Phase Noise: Systematic Construction of an Analytical Model
Encompassing Nonlinearity.” Their paper, which provides a straightforward
approach for trading off oscillator design parameters, is the
first paper in this digest.
The Draper Prize, endowed by Draper Laboratory and awarded by
the National Academy of Engineering, honors individuals who have
developed a unique concept that advances science and technology
while promoting the welfare and freedom of society. Since its
inception in 1988, the Draper Prize has recognized the developers
of the integrated circuit, the turbojet engine, FORTRAN, the Global
Positioning System, and the World Wide Web, to name a few. This
year, the Draper Prize was awarded to Frances H. Arnold and Willem
P.C. Stemmer for “directed evolution,” a process that mimics natural
mutation and selection to guide the creation of desirable properties
in proteins and cells in an accelerated laboratory environment.
On behalf of Draper Laboratory, I would like to congratulate both
recipients for their achievements, which are highlighted in greater
detail on the following pages.
Introduction by Dr. John Dowdle, Vice President of Engineering
5

The 2011 Charles Stark Draper Prize
6
The 2011 Charles Stark Draper Prize
The Charles Stark Draper Prize was established in 1988 to honor the memory of Dr.
Charles Stark Draper, “the father of inertial navigation.” The Prize was instituted by
the National Academy of Engineering and endowed by Draper Laboratory. The Prize
is recognized as one of the world's preeminent awards for engineering achievement,
and honors individuals who, like Dr. Draper, developed a unique concept that has
made significant contributions to the advancement of science and technology, as
well as the welfare and freedom of society.
For information on the nomination process, contact the Public Affairs Office at the
National Academy of Engineering at 202.334.1237.
The 2011 Charles Stark Draper Prize was awarded on February 22 at a ceremony in
Washington, D.C. to Frances H. Arnold and Willem P.C. Stemmer, who individually
contributed to a process called “directed evolution.” This process, now used in
laboratories worldwide, allows researchers to guide the creation of certain properties
in proteins and cells.
Directed evolution is postulated on the idea that the mutation and selection processes
that occur in nature can be accelerated in the laboratory to obtain specific, targeted
improvements in the function of single proteins and multiprotein pathways. Arnold
showed that randomly mutating genes of a targeted protein, especially enzymes,
would result in some new proteins with more desirable traits than they had before.
Selecting the best proteins and repeating this process multiple times, she essentially
directed the evolution of the proteins until they had the desired properties.
Stemmer concentrated on a different natural process for creating diversity and
concentrated on recombining preexisting natural diversity, a process he called “DNA
shuffling.” Rather than causing random mutations, he shuffled the same gene from
diverse but related species to create clones that were as good as or better than the
parental genes in a given targeted property.
An important aspect of directed evolution is that it provides a practical and costeffective
way to improve protein function. Previous efforts, especially those that
involved a design based on enzyme structures and the predicted effects of mutations,
were often not successful and were expensive and labor-intensive.
According to George Georgiou, a professor at the University of Texas at Austin.
“Arnold and Stemmer’s joint development of directed protein evolution was a
milestone in biological research. It is impossible to overstate the impact of their
discoveries for science, technology, and society; nearly every industrial product and
application involving proteins relies on directed evolution.”
Arnold is the Dick and Barbara Dickinson Professor of Chemical Engineering and
Biochemistry at the California Institute of Technology. She is listed as co-inventor
on more than 30 U.S. patents and has served as science advisor to more than 10
companies. In 2005, Arnold cofounded Gevo Inc., which develops new microbial

outes to produce fuels and chemicals from renewable resources.
She is among the few individuals who is a member of all three
membership organizations of the National Academies: The National
Academy of Engineering (2000), The Institute of Medicine (2004),
and the National Academy of Sciences (2008). She holds a B.S. in
Mechanical and Aerospace Engineering from Princeton University
(1979) and a Ph.D. in Chemical Engineering from the University of
California, Berkeley.
Stemmer is founder and CEO of Amunix Inc., which creates
pharmaceutical proteins with extended dosing frequency. In
2008, Amunix joined with Index Ventures to create Versartis Inc.
for the purpose of clinical development of three specific products
for the treatment of metabolic diseases. Stemmer has invented
other technologies that have led to other successful companies
and products. In 1993, he invented DNA shuffling and co-founded
Maxygen to commercialize the process. Prior to 1993, he was a
Distinguished Scientist at Affymax and a scientist at Hybritech.
In 2001, he invented the Avimer technology and founded Avidia
in 2003 to commercialize it; he was chief scientific officer of the
company until 2005. Stemmer has 68 research publications, 97 U.S.
patents, and is a recipient of the Doisy Award, the Perlman Award,
and the NASDAQ VCynic Award. He received his Ph.D. from the
University of Wisconsin-Madison in 1985.
The 2011 Charles Stark Draper Prize
Recipients of the Charles Stark Draper Prize
2009: Robert H. Dennard for the invention and development of Dynamic
Random Access Memory (DRAM)
2008: Rudolf Kalman for the development and dissemination of the
Kalman Filter
2007: Timothy Berners-Lee for creation of the World Wide Web
2006: Willard S. Boyle and George E. Smith for the invention of the charge-
coupled device (CCD)
2005: Minoru S. Araki, Francis J. Madden, Don H. Schoessler, Edward A.
Miller, and James W. Plummer for their invention of the Corona
reconnaissance satellite technology
2004: Alan C. Kay, Butler W. Lampson, Robert W. Taylor, and Charles P.
Thacker for the development of the Alto computer at Xerox's Palo
Alto Research Center (PARC)
2003: Ivan A. Getting and Bradford W. Parkinson for their technological
achievements in the development of the Global Positioning System
2002: Robert Langer for bioengineering revolutionary medical drug
delivery systems
2001: Vinton Cerf, Robert Kahn, Leonard Klienrock, and Lawrence Roberts
for their individual contributions to the development of the Internet
1999: Charles Kao, Robert Maurer, and John MacChesney for spearheading
advances in fiber-optic technology
1997: Vladimir Haensel for the development of the chemical engineering
process of “Platforming” (short for Platinum Reforming), which
was a platinum-based catalyst to efficiently convert petroleum into
high-performance, cleaner-burning fuel
1995: John R. Pierce and Harold A. Rosen for their development of
communication satellite technology
1993: John Backus for his development of FORTRAN, the first widely used,
general-purpose, high-level computer language
1991: Sir Frank Whittle and Hans J.P. von Ohain for their independent
development of the turbojet engine
1989: Jack S. Kilby and Robert N. Noyce for their independent development
of the monolithic integrated circuit
7

8
H•e jΦ
Resonator
φ = θ O - θ R
n r (t) n e (t)
e jΨr
θ O (t)
n(t)
Σ e jΨn
H•e jΦ
Ψ n (n(t))
θ O (t)
Amplifier
Σ Σ
Resonator
θ R (t)
e jΨn
Ψ n (n(t)) Ψ n (n e (t))
Engineers building new communications, navigation, and radar
systems seek to minimize phase noise, which can harm performance.
In a navigation system, phase noise can make it take longer for the
device to acquire the GPS satellite signal, which also drains power. In
communications and radar systems, phase noise can diminish range
and disrupt low-level signals.
This paper provides a general model for engineers studying design
trades who are seeking to understand how various noise sources,
as well as environmental disturbances, manifest as phase noise in
oscillators, which produce electronic signals.
This work could lead the way to improved oscillators that support
defense and intelligence customers’ communications and navigation
needs with more capable systems in smaller packages.
G
G•e jΦ
Amplifier
θ R (t)
Oscillator Phase Noise: Systematic Construction of an Analytical Model Encompassing Nonlinearity

Oscillator Phase Noise: Systematic Construction of
an Analytical Model Encompassing Nonlinearity
Paul A. Ward and Amy E. Duwel
Copyright ©2011 by the Institute of Electrical and Electronics Engineers (IEEE), published in IEEE Transactions on Ultrasonics and
Frequency Control, Vol. 58, No. 1, January 2011
Abstract
This paper offers a derivation of phase noise in oscillators resulting in a closed-form analytic formula that is both general and convenient
to use. This model provides a transparent connection between oscillator phase noise and the fundamental device physics and noise
processes. The derivation accommodates noise and nonlinearity in both the resonator and feedback circuit, and includes the effects of
environmental disturbances. The analysis clearly shows the mechanism by which both resonator noise and electronics noise manifest as
phase noise, and directly links the manifestation of phase noise to specific sources of noise, nonlinearity, and external disturbances. This
model sets a new precedent, in that detailed knowledge of component-level performance can be used to predict oscillator phase noise
without the use of empirical fitting parameters.
I. Introduction
This paper provides a predictive model for phase noise that does
not require fitting parameters, but instead is rigorously derived
from the fundamental dynamics of an oscillator loop. We build on
the work and insight of predecessors, including Leeson [1], Hajimiri
[2], and Rubiola [3] in particular. Section IIA briefly summarizes
key concepts from the literature that make our work possible. This
section also introduces a phase criterion that is new to oscillator
analysis and enables our modeling approach; specifically, the phase
criterion requires that the phase differences around a closed-loop
sum to zero instantaneously. Leveraging the insight of the linear
time variant (LTV) effect [2], we add a rigorous derivation that
provides a closed-form expression for the LTV gain function and
clearly shows the associated frequency-translation of the additive
noise as it manifests in phase noise (Sections IIB and IIC). By
capturing the LTV behavior in a general analytical model, one can
further appreciate the elegance of topologies such as Colpitts, in
which the feedback is periodically applied, to provide low-phasenoise
oscillators. Section IID shows how oscillator phase noise is
obtained from injected phase noise using the phase criterion. In this
section, we benchmark our approach by using it to derive Leeson’s
expression for phase noise. In Section IIE, we briefly address noise
that is derived directly from the resonator itself. Finally, Sections
IIF and IIG discuss the role of nonlinearity and time variance on
phase noise. Although it is already well known that nonlinearity in
active devices can degrade phase noise, we provide a formalism for
exactly how the mapping to phase noise occurs. In particular, we
show how the measurable property of voltage to phase conversion
in an amplifier can parameterize the resulting oscillator phase noise.
Our formalism also offers new results. We can predict for the
spectral density of the phase noise the 1/f3 corner frequency from
the amplifier properties and discuss why this frequency is usually
much lower than the 1/f corner of the individual amplifier. We also
provide an analytical expression for the phase noise resulting from
resonator nonlinearity and time variance and offer new insight into
an earlier finding that a nonlinear resonator can actually improve
the phase noise of an oscillator [4]. In particular, we show that
nonlinearity in the resonator reduces the phase noise that would
be predicted by the Leeson equation; however, we explain how
nonlinearity can add a new term to the phase noise because of the
coupling between frequency and amplitude. The derivation focuses
on mechanical or lumped-element-based resonators. Though our
approach is quite general, future work should explore the broader
applicability of these results, e.g., to photonic resonators.
II. Modeling of Phase Noise
A. Conceptual Basis of Model
The analysis relies heavily on two key insights. The first insight was
articulated by Hajimiri and Lee [2]. They recognized that phase
noise is an LTV function of the additive noise. Building on that
insight, the present work provides a closed-form solution to the
LTV gain function that is valid for small noise but can be extended
for arbitrarily large noise. The analysis leverages the concept of an
analytic signal that possesses the same phase (and amplitude) as
the oscillator signal at each respective node. This technique and its
application to oscillators are also described nicely in [3].
The second key to analyzing this system is a requirement that at an
instant in time, the phase at any point in the loop is well-defined
and single-valued with respect to a reference phase. The loop
topology then constrains the system such that the phase shifts
around the loop sum to zero. This includes phase shifts caused by
noise processes.
The constraint appears reminiscent of the Barkhausen criterion,
which describes the condition for steady-state oscillation. The
Barkhausen criterion, however, captures the condition that the
closed-loop transfer function has a resonance, so that there is a
finite response even with zero input. The statement is often made
Oscillator Phase Noise: Systematic Construction of an Analytical Model Encompassing Nonlinearity
9

that any disturbances in the loop not meeting the Barkhausen phase
criterion will decay in time. In the present analysis, a physically
realizable system must meet the zero-loop-sum phase condition
at all times and instantaneously because of the topology of being
in a loop. This seemingly intuitive condition has not been stated
explicitly in this context before. It allows one to use feedbacktheory-based
models, elegantly presented in [3], when the system
is fully linearized.
B. Determination of Injected Phase Noise
We consider the case in which a random signal n(t) is added to a
sinusoid of amplitude A, as shown in Figure 1. The only restriction
placed on n(t) is that it is wide-sense stationary (along with, by
extension, its Hilbert transform nˆ(t)), and thus we can define
its power spectrum. The noise phasor has random amplitude
and random phase. Thus, referring to Figure 1, it is clear that
the resultant phasor representing c(t) possesses both random
amplitude modulation and random phase modulation.
Figure 1. Graphic representation of an analytic signal.
10
Im{c(t)}
c(t)
Ae jw 0 t
noise
Re{c(t)}
It is customary with phase noise analysis to ignore amplitude noise
on the oscillator output. We shall follow this custom here. However,
we will consider the effects of amplitude noise within the loop on
the oscillator phase noise. As we shall see later, the amplitude noise
can excite nonlinear effects that can increase phase noise.
The phasor c(t) is represented as an analytic signal. In Appendix
A, we show how the amplitude and phase of this signal are related
to n(t). Figure 2 represents the analytic signal at different nodes of
an oscillator block diagram at a snapshot in time. We consider that
the noise n(t) is referred to a single node and is due to the feedback
electronics. An explicit phase shift Ψ is introduced to identify the
n
noise-derived phase shift introduced through the LTV mapping of
n(t) into the loop. We use the analytic signal formalism to show how
Ψ n (t) is derived from n(t).
H•e jΦ
Resonator
φ = θ O - θ R
Figure 2. Simplified block diagram for an oscillator.
Oscillator Phase Noise: Systematic Construction of an Analytical Model Encompassing Nonlinearity
θ O (t)
n(t)
Σ e jΨn
Ψ n (n(t))
G
Amplifier
θ R (t)
From our analytic signal representation, we can express the injected
phase noise as
Ψ (t) = tan n -1 – tan-1 A sin(w t) + n o ,
^ (t) sin(w t) o
A cos(w t) + n(t) cos(w t)
o o
where w o is the time-average oscillator frequency. Equation (1) is
exact, but not particularly convenient. To obtain a working equation
for phase noise, it can be expanded in a Taylor series. Considering
that the noise is small compared with the signal amplitude, we can
keep only the first-order terms of the expansion, resulting in
n
Ψ (t) n ≅ cos(w t) – sin(w t).
o o ^ (t)
n(t)
A
A
We see that the phase noise is an LTV function of additive electronics
noise n(t). Furthermore, the zero-loop-sum criterion requires that
φ = -Ψ . Equation (2) is important because it forms the basis for the
n
mapping of additive noise to feedback phase noise.
It should be noted that this analysis implicitly assumes that there is
no parametric modulation in the feedback electronics. This is not
a linear time invariant (LTI) effort and is addressed in more detail
later in the paper.
In addition, all oscillators possess some nonlinearity that keeps
the amplitude from growing without bound. This nonlinearity may
be intrinsic or may be added as part of the design. In Section IIG,
we address the exacerbation in phase noise for the specific case
in which active amplitude stabilization is used and the resonator
possesses coupling between amplitude and frequency.
C. Spectrum of Injected Phase Noise
Because we are dealing with random signals, we wish to work in
terms of frequency spectra. To that end, we wish to compute the
spectrum of the injected phase noise. Note that the spectrum of
the phase noise is not a signal power spectrum per se, but instead
represents the distribution of phase power as a function of
frequency, having units of Hz-1 .
(1)
(2)

The derivation of injected phase spectrum is rather lengthy, and
therefore has been included as Appendix B. The result is
1
S (w) Ψn ≅ [S (w – w ) + S (w + w )],
n o n o A2 (3)
where S (w) is the double-sided spectrum of the additive
n
electronics noise (in V2 /Hz) and w is the baseband frequency.
In words, the double-sided injected phase noise spectrum is
proportional to the double-sided additive noise spectrum shifted
toward positive frequency by w plus the double-sided electronics
o
noise spectrum shifted toward negative frequency by w . Equation
o
(3) also shows that the injected phase noise is given by the ratio
of additive amplifier noise power to signal power, as expected. For
the case in which the amplifier noise is attributed to white noise
[S (w) = S ], this ratio can be put into more fundamental units by
n nw
dividing double-sided available noise power density by available
signal power:
2Snw A2 2Fk T B S (w) Ψn ≅ = ,
where F is the amplifier noise factor and P s is the signal power.
D. Oscillator Phase Noise and the Leeson Formula
The oscillator phase noise, which we will also refer to as the readout
phase noise θ (t), is the sum of two terms: the phase noise of the
R
resonator output θ (t) and the phase noise injected, Ψ (t). This can
o n
be seen by walking through the loop in Figure 2.
The phase noise of the resonator output is also the time integral of
the frequency noise of the resonator output. That is,
P s
θ R (t) = Ψ n (t) + ∫ t
w o (t)dt.
–∞
In the case in which the resonator is LTI, the frequency noise of the
resonator output signal is given by
w o (t) =
∂φ(w o )
∂w
-1
φ(t).
Thus, the general expression for phase noise of an oscillator
employing an LTI resonator is
∂φ(w ) o θ (t) = Ψ (t) +
R n
-1
∫ t -1
∫ ∂w
t
∂w –∞
where the transfer function of the resonator is
H(w) = |H(w)|e jφ(w) .
φ(t)dt,
Very often, the resonator is a second-order LTI system. In this case,
the phase slope at resonance is
∂φ(w o )
∂w
-2Q
= ,
w o
Oscillator Phase Noise: Systematic Construction of an Analytical Model Encompassing Nonlinearity
(4)
(5)
(6)
(7)
(8)
(9)
where Q is the loaded quality factor of the resonator. Considering
also the phase criterion that φ = -Ψ n , the phase noise is given by
θ R (t) = Ψ n (t) +
∫ t
wo Ψ (t)dt.
2Q n
–∞
(10)
This equation is a time-domain representation of the phase noise
from the oscillator. It leads directly to the Leeson equation because
it shows how the output phase noise is derived from the sum of
injected phase noise plus its integral.
Note that determination of the time-domain oscillator phase noise
involves evaluation of a running integral of the injected phase
noise. We run into a difficulty when attempting to integrate white
noise because the integral tends to grow without bound. We can
circumvent this difficulty by considering the integration time to be
finite. To accommodate this restriction in the frequency domain,
we will restrict validity of the spectrum of the integrated feedback
phase noise, so that w = 0 is excluded.
The spectrum of the oscillator phase noise is found by taking the
power spectral density (PSD) of (10) subject to the integration time
restriction, given by
S θR (w) ≅ S Ψn (w) 1 +
∂φ(w o )
∂w
- 2
1
w 2
, |w| > 0.
(11)
For an oscillator having a second-order LTI resonator, the phase
noise spectrum is given by
S θR (w) ≅ S Ψn (w) 1 +
w o
2Qw
2
.
(12)
It is clear from these expressions that injected phase noise is a
critical determinant of oscillator phase noise, particularly in the
case in which the resonator has a finite phase slope at resonance.
In the case in which only white additive noise with a PSD of S n = S nw is
considered, the injected phase noise becomes S Ψn (w) = 2S nw /A 2 , and
(12) parallels the familiar Leeson result.
E. Inclusion of Resonator Noise
Refer to Figure 3 on the following page, which shows an oscillator
loop having electronics noise and resonator noise. We can use the
results obtained earlier for mapping of additive noise to phase noise
and for generating the corresponding phase noise spectrum. The
resonator noise will contribute to injected phase noise and injected
amplitude noise, but only the phase noise is important in the case of
a linear resonator. If we assume that the resonator input is a sinusoid
of amplitude B plus additive white noise n r (t) having a PSD of S rw ,
the injected phase spectrum including both electronics noise and
resonator noise is given by
2Srw B2 1
S (w) Ψ ≅ [S (w – w ) + S (w + w )] + .
n o n o
A 2
(13)
For simplicity, we took the resonator noise to be white, though it
can be any stationary noise process. Section IIF will show how a
11

n r (t) n e (t)
nonwhite noise process in the electronics (1/f noise in this case)
contributes to oscillator phase noise, and identical steps can be
applied to analyze the impact of nonwhite resonator noise on phase
noise.
By separating out the electronics phase noise from resonator phase
noise, it is possible to use material-based models for the spectra
of each component and see how the noise propagates through a
given system. For the case in which the resonator is mechanical, a
fundamental noise source is the white Brownian force associated
with the finite mechanical loss in the resonator [5]. If given an
equivalent circuit for the mechanical resonator, the model for this
noise term is exactly like Johnson noise in a resistor [6]:
12
H•e
Ψ (n(t)) n Ψ (n (t))
n e jΦ
e jΨn
θ (t)
O
e jΨr
Σ Σ
Resonator
2S rw = 4k B TR x .
G•e jΦ
Amplifier
θ R (t)
Figure 3. Simplified block diagram for an oscillator. This model
explicitly includes electronics noise, resonator noise, and parametric
phase shifts in the amplifier.
(14)
R is the equivalent circuit resistance at resonance and depends
x
on both the resonator Q and the design-specific electromechanical
coupling.
F. Effects of Nonlinearities and Parametric Sensitivities in
the Amplifier
For an LTI resonator with a feedback network free of parametric
modulation, the expressions developed thus far for the phase noise
spectrum, (12) and (13), are as accurate as (and, without resonator
noise, equate to) the Leeson formula. Oscillator phase noise
predictions based on Leeson’s equation typically underestimate
the phase noise below about 1 kHz, in part because of the effect of
flicker noise.
In general, the excess phase noise will be caused by non-LTI
effects. In the electronics, we refer to these effects as parametric
modulation, which can be driven by additive noise, amplitude noise,
or effects such as temperature variation and vibration.
We will model the noise caused by parametric modulation by
including an additive term SΦ(w) in the expression for feedback
electronics phase. Important components of SΦ(w) are the
terms produced by amplitude noise, power supply variations,
and temperature. The feedback amplifier will generally include
transistors, and the transistor parameters are signal-dependent.
Therefore, the phase shift imparted by the transistor amplifier will
change with bias point or signal amplitude, as well as temperature.
Low-frequency additive noise (such as flicker) produces a lowfrequency
variation in the bias point, which in turn produces a
low-frequency parametric modulation and a corresponding lowfrequency
phase modulation. This is responsible for the propagation
of additive flicker noise to flicker noise in feedback phase, which
produces a 1/f 3 oscillator phase noise PSD. Considering the effects
of additive noise-to-phase conversion and amplitude noise-to-phase
conversion, the spectral density caused by parametric modulation
is given by
S Φ (w) ≅
∂Φ(w)
∂n
2
S n (w) +
2
S A (w).
Oscillator Phase Noise: Systematic Construction of an Analytical Model Encompassing Nonlinearity
∂Φ
∂A
(15)
Note that the parametric modulation coefficients of (15) are
easy to determine either from circuit simulation or by empirical
measurement at the circuit level.
To capture the phase noise caused by parametric modulation in the
electronics, Figure 3 explicitly identifies a parametric phase shift in
the amplifier, Φ(t). In this case, the spectrum of the feedback phase
becomes:
S θf (w) = S Ψ (w) + S Φ (w)
2S rw
1
A2 ≅ [S (w – w ) + S (w + w )] + + S (w).
n o n o Φ B2 (16)
Note that (16) makes it clear that feedback phase noise is composed
of the sum of injected phase noise (the phase produced by the
LTV mapping of additive noise) and parametric phase noise. The
associated oscillator phase noise of (11) generalizes to
∂φ(w ) o S (w) θR ≅ S (w) 1+ , |w| > 0.
θf -2
1
∂w
w 2
(17)
To elaborate on the propagation of additive flicker noise to flicker
feedback phase, note that additive flicker noise does not convert
to appreciable phase noise without a nonlinear element coupling
additive noise to phase noise (i.e., without parametric modulation).
For example, let the additive flicker noise PSD be
Kfe S (w) n ≅ .
|w|
(18)
In the absence of direct conversion of additive noise to phase noise,
the corresponding feedback phase noise spectrum is
S (w)
1
θf ≅ + ≅ 0, 0 < |w| « w . o |w – w | |w + w | o o (19)
A 2
K fe
Thus, in this case, we are insensitive to additive flicker noise because
of the effect of the LTV mapping of additive noise to phase noise
(the up-modulation).
However, in the case in which the feedback electronics possesses
parametric modulation that results in direct coupling between
additive noise voltage and phase (for example, because of biasdependent
semiconductor devices in the electronics), there will be
K fe

a flicker component in the feedback phase noise spectrum given by
Kfp S (w) Φ ≅ .
|w|
At very low frequency, the phase noise spectrum becomes:
S θR (w) ≅
∂φ(w o )
∂w
-2
K fp
|w| 3
.
Oscillator Phase Noise: Systematic Construction of an Analytical Model Encompassing Nonlinearity
(20)
(21)
Thus, the presence of direct coupling between voltage (or current)
and phase will result in a sensitivity to flicker noise, producing a 1/
f 3 close-in slope to the phase noise spectrum. The corner frequency
of the oscillator phase noise, using the variables introduced in this
analysis, becomes
w c.osc ≅
∂Φ(0)
∂n
2
A 2
w , c.amp 2
(22)
where w is the corner frequency of the electronics noise, at which
c.amp
the flicker component (K /|w|) equals the white noise component
fe
(2S /A nw 2 ). Eq. (22) represents a significant deviation from the
convention: the corner frequency where 1/f3 oscillator noise begins
to dominate is not equal to the amplifier corner frequency, but is
scaled by the amplifier nonlinearity. Though many have noted that
the oscillator corner can be significantly lower than the amplifier
corner, this analysis offers a specific closed-form model. It should
be noted that K can be reduced by several techniques that amount
fp
to linearization through the use of degenerative feedback in the
electronics. In addition, the electronics flicker coefficient, K is fe
process-dependent, but for a given process, can be reduced through
device scaling.
The phase shift in the amplifier section can also fluctuate because
of temperature, vibration, and other effects. Device models for
fluctuation in the amplifier components and their resulting phase
shifts can be inserted into (15)-(17) to identify the impact on phase
noise.
G. Effects of Nonlinearities and Parametric Sensitivities in the
Resonator
In its simplest form, the oscillator phase noise includes only the
additive electronics noise mapped into phase noise through the
LTV transformation and passed through the resonator in a closed
loop. This is expressed in (11). Equations (16) and (17) generalize
this to include more noise sources feeding into the loop.
The derivations have intentionally left the resonator phase slope
at resonance as a general function. For mechanical resonators that
have nonlinear response characteristics, the slope near w can o
become higher than that of a linear device and even bifurcate into
a multivalued function [7]. It has been shown that some types of
nonlinearity can actually improve the phase noise of the device as
long as the operating point can be well-defined [4].
In addition to being affected by feedback phase, the phase noise of
an oscillator can also be affected by variations in natural frequency.
The natural frequency can be affected by environmental influences,
such as temperature and vibration, or by a nonlinear coupling
between oscillator amplitude and natural frequency.
To define the parametric noise caused by shifts in the resonator
natural frequency, we must make a distinction between resonator
oscillation frequency noise w (t) and resonator natural frequency
o
noise w (t). The natural frequency noise is the resonator oscillation
n
frequency noise in the absence of feedback phase noise. The
resonator oscillation frequency noise can be expressed as (see (6))
w o (t) ≅ w n (t) +
∂φ(w o )
∂w
-1
φ(t).
(23)
We can express the readout phase noise in a form that is explicit in
feedback phase noise and natural frequency noise as follows:
θ R (t) = Ψ n (t) +
-1
∫ t
φ(t)dt + ∫ t
∂w –∞
–∞
∂φ(w o )
w n (t)dt.
(24)
Notice that this reduces to the expression for the LTI resonator
when there is no natural frequency noise.
With this effect, together with the resonator noise, the Leeson
equation (12) is more generally written as
S θR (w) ≅ S θf (w) 1 +
w o
2Qw
2
S (w)
+ wn .
w 2
(25)
Mechanical resonators often exhibit amplitude-frequency
sensitivity at high amplitudes, which can be due to materials,
geometric, or even electrostatic effects [8]-[10]. Thus, stochastic
variation of the resonator amplitude can convert directly to phase
noise. In general, the phase noise contribution caused by amplitudefrequency
coupling can be written as
S wn (w) ≅
∂w n
∂X
2
S X (w),
(26)
where in this case X is the amplitude of the resonator. Typical values
for mechanical nonlinearity are often quoted in terms of power and
range from 10 -9 /μW for AT- and BT-cut quartz, to 10-11 /μW for SCcut
quartz [8].
Resonator amplitude noise will depend, in part, on the amplitude
control approach. In the case in which an amplitude control loop
is designed to control the oscillator output amplitude to a specific
value, the amplitude noise is given by the amplitude noise injected
by the oscillator feedback electronics, provided that we neglect
noise added by the amplitude control circuitry. In this case, we
can determine oscillator amplitude noise using the analytic signal
formulation in a way that parallels our derivation of injected phase
noise. Doing so results in injected amplitude noise spectrum given
by
1
S (w) = [S (w – w ) + S (w + w )]. (27)
An ne o ne o 2
13

The resulting natural frequency noise spectrum is given by
14
S wn (w) ≅
∂w n
∂X
∂X
∂A
2 2
S An (w).
(28)
Therefore, amplitude noise will increase oscillator phase noise in
the case of a resonator with amplitude-frequency coupling, and
white amplitude noise will increase 1/f2 phase noise in this case.
Finally, external influences on the resonator frequency, such as those
described by [11], can be inserted into the phase noise predictions
using (25). For example, if random vibration induces changes in the
natural frequency of the resonator, we may write:
S wn (w) ≅
∂w n
∂g
2
S g (w),
(29)
where S g (w) is the vibration PSD in g 2 /Hz. This expression is
consistent with well-established results [8], [11], [12]. Typical
frequency sensitivities (Δw/w o ) are in the range of 10 -10 /g, and
typical rms vibration levels inside a quiet building are given on the
order of 20 mg [11], resulting in substantial phase noise that cannot
be neglected in the interpretation of real test data.
H. Single-Sideband Noise Spectral Density
Phase noise is typically expressed in terms of decibels per hertz with
respect to the carrier power (dBc/Hz) using the single-sideband
noise spectral density, defined per IEEE Standard 1139–2008 [13] as
1
L(w) = S (w),
θR,SSB 2
(30)
where S (w) is the single-sideband spectrum and S (w) =
θR,SSB φ,SSB
2S (w), w > 0. Appendix C discusses the definitions in more detail.
φ
Hereafter, we replace w by Δw in the phase noise expression to
emphasize the fact that the frequency to which we refer is the offset
from the carrier frequency.
In the case in which the resonator is LTI, the electronics do not
include parametric modulation, where we neglect resonator noise
and consider only the electronics noise, the single-sideband noise
spectral density (in dBc/Hz) is given by
L(Δw) ≅ 10 log
. 1 +
(S n (Δw – w o ) + S n (Δw + w o ))
A 2
∂φ(w o )
∂w
1
Δw
-2 2
I. General Expression for Single-Sideband Noise
Spectral Density
For the general case, the readout phase spectrum is given by
S θR (w) ≅ S θf (w) 1 +
∂φ(w o )
∂w
-2
.
1
w 2
S (w)
+ wn ,
w2 (31)
(32)
where S θf was defined in (16). Assuming additive white resonator
noise, stationary electronics noise, a non-LTI resonator, and
feedback electronics with parametric modulation, the singlesideband
noise spectral density is given by (33), see above, where
w o is the oscillator frequency; 0 < |Δw|

L(Δw)
-110
-120
-130
-140
-150
oscillator total L
oscillator noise due to amplifier
white noise:
L =10•log 2s w nw o
A2 1+
2QΔw
amplifier white noise floor:
L =10•log
-160 1 10 100 10 3
w3dB
Δw
(a) (b)
Figure 4. Plots of the total oscillator phase noise spectral density (red), overlaid with plots emphasizing the role of white noise (a) and 1/f
noise (b) in the total phase noise. Plot (a) focuses on the role of white noise in the feedback phase. The noise floor of the amplifier is given,
and the term in the total L caused by white noise in the feedback phase is plotted as the solid blue line. Plot (b) focuses on the role of the
amplifier nonlinearity in response to 1/f noise. The amplifier L caused by 1/f noise is shown as a dashed line, together with the amplifier white
noise floor. The solid blue line shows how amplifier 1/f noise contributes to feedback phase and plots oscillator phase noise caused by this
term. It is clear from the plot that the 1/f contribution dominates the oscillator’s total response at low frequencies.
Cases can be run that show further increases in close-in noise if we
introduce resonator nonlinearity. Finally, even though vibration
was neglected, we note that even a small amount of vibration has a
significant effect on the phase noise.
III. Conclusion
The past two decades have seen substantial progress in the
understanding of fundamental phase noise processes and modeling
tools. Although many basic mechanisms were identified in the early
days of radio, there have been outstanding recent contributions
in advanced numerical tools and insightful analytical modeling.
This work adds to the analytical modeling literature, providing an
intuitive model for oscillator phase noise that allows convenient
generalization to cases where the resonator is non-LTI or the
electronics include parametric modulation.
This paper began by postulating that the instantaneous incremental
phase shifts around an oscillator loop sum to zero. This led to the
assertion that the instantaneous resonator phase noise is equal
in magnitude and opposite in sign to the feedback phase noise.
Oscillator phase noise was then expressed as the sum of the
feedback phase noise and the running integral of resonator output
frequency. Resonator output frequency for the case of an LTI
resonator was found by multiplying the feedback phase noise by the
reciprocal of the resonator phase slope evaluated at the resonator
oscillation frequency.
The work showed that electronics flicker noise impacts close-in
phase noise only if the electronics includes parametric modulation,
2
in which case, the feedback phase noise consists of both injected
phase noise (noise passed through the LTV mapping) and
parametric phase noise. The flicker noise results in a 1/f3 component
in the close-in phase spectrum. Parametric phase noise may also
have contributions from variations in amplitude, power supply, and
temperature. Additional phase noise can result if the resonator is
non-LTI. This additional phase noise is associated with perturbation
of the resonator natural frequency. The natural frequency can be
disturbed by resonator amplitude variations (in the case of a nonlinear
resonator), and by effects such as vibration, temperature, and drift.
Appendix A
Analytic Signal Representation of Oscillator Signal
Fundamental to our approach for determining phase noise is the
recognition that any real signal s(t) has a complex counterpart
(called its analytic signal) that possesses the same instantaneous
amplitude and the same instantaneous phase as s(t). The analytic
signal will be denoted c(t) and is given by
c(t) = s(t) + js^(t), (A1)
where sˆ(t) is the Hilbert transform of s(t). For our purposes, the
Hilbert transform is best viewed as the output of a linear filter that
produces a phase shift of -90 deg at all positive frequencies, is
Hermitian, and has unity gain. The transfer function of the Hilbert
transformer is given by
H (w) = -j sgn(w), (A2)
H
Oscillator Phase Noise: Systematic Construction of an Analytical Model Encompassing Nonlinearity
2S nw
A 2
L(Δw)
-100
-120
-140
-160
amplifier 1/f: L =10•log
wc.amp
K fe
A 2 Δw
-180
1 10 100 3 10
wc.osc oscillator noise due to amplifier 1/fnoise:
oscillator total L
L =10•log Kfp 1+
Δw
wo 2QΔw
2
15

where
16
sgn(w) =
-1, w < 0
0, w = 0
+1, w > 0. (A3)
Consider the case in which a random noise signal n(t) is added to a
sinusoid of amplitude A. The only restriction placed on n(t) is that it
is wide-sense stationary, and thus we can define its power spectrum.
The noisy sinusoid is given by
s(t) = A cos(w t) + n(t). (A4)
o
The analytic signal is given by
c(t) = A cos(w o t) + jA sin(w o t) + n(t) + jn^(t). (A5)
The analytic signal can be expressed in a polar form as
c(t) = Aejwot + n2 (t) + n^ 2 (t)e j tan-1 (n^(t)/n(t)) . (A6)
We see that the analytic signal is the sum of two phasors, one
corresponding to the signal and another corresponding to the noise.
The signal phasor has amplitude A and is rotating counterclockwise
at a constant rate of w . The noise phasor has random amplitude
o
and random phase. The resultant phasor representing c(t) possesses
both random amplitude modulation and random phase modulation.
Appendix B
Derivation of Feedback Phase Spectrum
The injected phase is given by
n^ (t)
n(t)
Ψ (t) n ≅ cos(w t) – sin(w t).
o o A
A (B1)
The autocorrelation function (ACF) of the injected phase is given by
n^(t)
A
n(t)
A
R Ψn (t) = cos(w o t) – sin(w o t)
. n^(t + t)
n(t + t)
cos(w (t + t)) – sin(w (t + t)) ,
o o A
A (B2)
where the angle brackets denote the expected value operator. The
ACF can be expanded as
1
R (t) = 〈n^(t)n^(t + t) cos w t cos w (t + t)
Ψn A o o 2
+ n(t)n(t + t) sin w t sin w (t + t)
o o
– n(t)n^(t + t) sinw t cos w (t + t)
o o
– n^(t)n(t + t) cos w t sin w (t + t)〉. o o (B3)
We can eliminate terms that average to zero, yielding
1
R (t) = cos w t〈n^(t)n^(t + t)〉
Ψn o
2A 2
1
+ cos w t〈n(t)n(t + t)〉
o
2A 2
1
+ sin w t〈n(t)n^(t + t)〉
o
2A 2
1
– sin w t〈n^(t)n(t + t)〉.
2A o 2 (B4)
It can be shown that [15]
〈n(t)n^(t + t)〉 = –〈n^(t)n(t + t)〉. (B5)
Therefore, the cross-correlation terms add, and we have
1
R (t) = cos w t〈n^(t)n^(t + t)〉
Ψn o
Oscillator Phase Noise: Systematic Construction of an Analytical Model Encompassing Nonlinearity
2A 2
1
+ cos w t〈n(t)n(t + t)〉
o
2A 2
1
+ sin w t〈n^(t)n(t + t)〉.
A o 2 (B6)
1
R (t) = cos w t〈n^(t)n^(t + t)〉
Ψn o
2A 2
1
+ cos w t〈n(t)n(t + t)〉
o
2A 2
It can also be shown that [15]
1
+ sin w t〈n^(t)n(t + t)〉.
2A o 2 (B7)
sn^ (w) = n -jS (w) w < 0
nn
jS (w) w < 0.
nn
(B8)
Now, note the following Fourier transform property applied to the
autocorrelation function:
FT 1
R (t) R (t)↔ S (w) 1 2 1 * S (w), 2
2p (B9)
where the asterisk denotes the convolution operator. From (B7), we
have
1
1
R (t) = R Ψn n^ (t) cos w t + R (t) cos w t
o n o
2A 2
1
+ Rn^ (t) sin w t
n o
A 2
Using (B9) and (B10), we obtain
2A 2
1
S (w) = FT{R Ψn n^(t)} * FT{cos w t} o
4pA 2
1
+ FT{R (t)} n * FT{cos w t} o
4pA 2
(B10)
1
+ FT{Rn^ (t)} n * FT{sin w t}. o 2pA2 (B11)
This equation can be rewritten as
1
S (w) = S (w) Ψn n * FT{cos w t} o
Note that:
4pA 2
1
+ S (w) n * FT{cos w t} o
4pA 2
1
+ jS (w) sgn(w) n * FT{sin w t}. o 2pA2 (B12)
FT{cos w o t} = p[d(w – w o ) + d(w + w o )] (B13)

and
FT{sin w o t} = jp[d(w + w o ) – d(w – w o )]. (B14)
Simplifying (B12), we obtain
1
S (w) = S (w) Ψn n * FT{cos w t} o
2pA 2
j
– S (w) sgn(w) n * FT{sin w t}. o 2pA2 (B15)
Finally, we obtain the injected phase noise spectrum:
1
S (w) = [S (w – w )[1 – sgn(w – w )]]
Ψn n o o
2A 2
1
+ [S (w + w )[1 + sgn(w + w )]].
n o o
2A 2
(B16)
Because we are concerned with lower frequencies, we know that for
our frequencies of interest
0 < | w| < w o . (B17)
Thus, the injected phase noise spectrum can be approximated as
1
S (w) Ψn ≅ [S (w – w ) + S (w + w )].
A n o n o 2
Appendix C
Noise-Carrier Ratio from Phase Spectrum
We can define the noise-carrier ratio (NCR) as
(B18)
single sideband PSD
NCR(w) ≡ .
carrier power
(C1)
This definition is useful when attempting to measure phase noise
using a spectrum analyzer. Although NCR has been replaced with
the more general single-sideband noise spectral density denoted
by L(w) [13] (also explained in [3]), the authors believe it may
be useful to provide the derivation between phase spectrum and
noise-carrier ratio, because NCR(w) and L(w) are identical for
small phase noise.
Consider a sinusoid with small phase modulation
e(t) = cos(w o t + φ(t)). (C2)
The signal can be approximated (using a Taylor expansion) as
e(t) ≅ cos(w o t) – φ(t) sin(w o t). (C3)
The sideband term can be approximated as
The sideband spectrum is found from
or
e SB (t) ≅ –φ(t) sin w o t. (C4)
1
S (w) SB ≅ S (w) φ * FT{sin w t} o 2p
Oscillator Phase Noise: Systematic Construction of an Analytical Model Encompassing Nonlinearity
(C5)
-j
S (w) SB ≅ S (w) sgn(w) φ * jp[d(w + w ) – d(w – w )].
o o 2p (C6)
Therefore,
1
S (w) SB ≅ [S (w + w ) + S (w – w )].
φ o φ o 2
(C7)
Because the phase spectrum will always be a low-pass function,
we will treat it here as a strictly band-limited low-pass function for
simplicity. Then from (C7) and Figure C1, it is clear that
1
2 S (w - w )
φ 0
- w 0
1
S (w + w) SB o ≅ S (w). φ 2
S SB (w)
S φ (w)
+ w 0
(C8)
S φ (w + w 0 )
Figure C1. Phase spectrum and corresponding power spectrum for a
phase-modulated sinusoid.
Therefore,
1
2
S (w + w)
SB o
NCR(w) ≡
P ≅ S (w), w >0.
φ
o
(C9)
Note that because the signal amplitude in (C2) was normalized to
unity, then P = 1/2.
o
Finally, we note that to avoid any confusion, because frequency w
in the NCR expression represents the frequency deviation from the
carrier frequency, we will replace w by Δw in the NCR expression,
so that:
NCR ≅ S (Δw). (C10)
φ
Figure C1 shows a representative baseband phase spectrum and the
corresponding spectrum for a phase-modulated sinusoid having
normalized amplitude. For analytical purposes, double-sided
spectra are used in this manuscript unless specifically mentioned
otherwise. The IEEE Standard 1139 reports phase noise spectra in
terms of the single- sided spectrum as [13]
1
L(Δw) = S (Δw) = S (Δw), Δw > 0,
φ,SSB φ 2
(C11)
where, per the usual definition, the single-sided spectrum is defined
only when Δw > 0 and is given by S,SSB(Δw) = 2S(Δw).
Acknowledgments
The authors wish to thank The Charles Stark Draper Laboratory,
Inc. for supporting this work, and especially Jeff Lozow of Draper
Laboratory for verifying the derivation in Appendix B.
17

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18
Oscillator Phase Noise: Systematic Construction of an Analytical Model Encompassing Nonlinearity

Paul A. Ward is Laboratory Technical Staff (highest technical tier) at The Charles Stark Draper Laboratory. He has
extensive experience in the development of high-performance electronics for a wide array of systems. He has been
with Draper for 25 years and has developed innovative circuits and signal processing to support precision signal
references, fiber-optic gyroscopes, Microelectromechanical System (MEMS) gyroscopes and accelerometers,
strategic radiation-hard inertial instruments, and other instruments and systems. He received Draper’s
Distinguished Performance Award in both 1994 and 1997, as well as Draper’s Best Patent Award in 1996, 1997,
and 1998. Mr. Ward has managed Draper’s Microelectronics group, Analog and Power Systems group, and Mixed-
Signal Control Systems group. He currently holds 22 U.S. patents with several in application and has coauthored
numerous papers. Mr. Ward holds B.S. and M.S. degrees in Electrical Engineering from Northeastern University.
Amy E. Duwel is Group Leader for RF and Communications at The Charles Stark Draper Laboratory after many
years managing Draper’s MEMS Group. Her technical interests focus on microscale energy transport and on the
dynamics of MEMS resonators in application as inertial sensors, RF filters, and chemical detectors. Dr. Duwel
received a B.A. in Physics from the Johns Hopkins University and M.S. and Ph.D. degrees in Electrical Engineering
and Computer Science from the Massachusetts Institute of Technology (MIT).
Oscillator Phase Noise: Systematic Construction of an Analytical Model Encompassing Nonlinearity
19

20
Due to the limited lifespan of artificial joints and the ineffectiveness of current
drug regimens, patients below the age of 65 with end-stage osteoarthritis
often live with severe pain and disability. Researchers from Cytex
Therapeutics, Draper, and MIT are collaborating on the project under a grant
from the national Institutes of Health with the hope to develop treatments
that replace damaged tissue at the joint surface with a living cartilage tissue
substitute. Cytex and MIT began working on the project in 2007, and Draper
joined in 2010.
Current cell-based cartilage repair procedures are limited to small defects.
The researchers believe that using a mechanically functional biomaterial
scaffold may enable repair of the entire joint surface while also allowing loadbearing
associated with normal daily activities.
The researchers also expect that using a live cell component may allow the
new cartilage tissue to maintain itself. This may enable improvement over
current artificial joint prostheses, which tend to wear over time, resulting in
an effective life span of approximately 10 to 20 years.
Young patients with end-stage osteoarthritis stand to benefit the most from
this work, which could also bring down the high cost associated with treating
end-stage osteoarthritis by reducing the number of revision surgeries and
the need for drugs to treat pain. The ability to postpone replacement of an
osteoarthritic joint for 5 to 10 years would be highly welcome by both patients
and payers.
The next step is to demonstrate the long-term efficacy of this type of implant
in animal models. Cytex Therapeutics expects that the work will be ready for
human clinical trials in the 2015 to 2020 time frame.
In Vitro Generation of Mechanically Functional Cartilage Grafts . . .

In Vitro Generation of Mechanically Functional Cartilage
Grafts Based on Adult Human Stem Cells and 3D-Woven poly
(ε-caprolactone) Scaffolds
Piia K. Valonen, Franklin T. Moutos, Akihiko Kusanagi, Matteo G. Moretti, Brian O. Diekman,
Jean F. Welter, Arnold I. Caplan, Farshid Guilak, and Lisa E. Freed
Copyright ©2009 by Elsevier Ltd. All rights reserved. Published in Biomaterials, Vol. 31, January 19, 2010, pp 2193 - 2200.
Abstract
Three-dimensionally (3D) woven poly(ε-caprolactone) (PCL) scaffolds were combined with adult human mesenchymal stem cells
(hMSC) to engineer mechanically functional cartilage constructs in vitro. The specific objectives were to: (1) produce PCL scaffolds
with cartilage-like mechanical properties, (2) demonstrate that hMSCs formed cartilage after 21 days of culture on PCL scaffolds, and
(3) study the effects of scaffold structure (loosely vs. tightly woven), culture vessel (static dish vs. oscillating bioreactor), and medium
composition (chondrogenic additives with or without serum). Aggregate moduli of 21-day constructs approached normal articular
cartilage for tightly woven PCL cultured in bioreactors, were lower for tightly woven PCL cultured statically, and lowest for loosely woven
PCL cultured statically (p < 0.05). Construct DNA, total collagen, and glycosaminoglycans (GAG) increased in a manner dependent on
time, culture vessel, and medium composition. Chondrogenesis was verified histologically by rounded cells within a hyaline-like matrix
that immunostained for collagen type II but not type I. Bioreactors yielded constructs with higher collagen content (p < 0.05) and more
homogenous matrix than static controls. Chondrogenic additives yielded constructs with higher GAG (p < 0.05) and earlier expression of
collagen II mRNA if serum was not present in medium. These results show the feasibility of functional cartilage tissue engineering from
hMSC and 3D-woven PCL scaffolds.
Introduction
Degenerative joint disease affects 20 million adults with an
economic burden of over $40 billion per year in the U.S. [1].
Once damaged, adult human articular cartilage has a limited
capacity for intrinsic repair [2] and hence injuries can lead to
progressive damage, joint degeneration, pain, and disability. Cellbased
repair of small cartilage defects in the knee joint was first
demonstrated clinically 15 years ago [3]. Many cartilage tissue
engineering studies use chondrocytes as the cell source [4], [5],
however, this approach is challenged by the limited supply of
chondrocytes, their limited regenerative potential due to age,
disease, dedifferentiation during in vitro expansion, and the
morbidity caused by chondrocyte harvest [6]. Therefore, other
studies use mesenchymal stem cells (MSC) as the cell source [7],
[8], as these stem cells can be harvested safely by marrow biopsy,
readily expanded in vitro, and selectively differentiated into
chondrocytes [9].
Clinical translation of tissue engineered cartilage is currently
limited by inadequate construct structure, mechanical function,
and integration [2], [10]. Currently, most tissue engineered
constructs for articular cartilage repair possess cartilage-mimetic
material properties only after long-term (e.g., 1-6 months) in vitro
culture [5], [11], [12]. This lack of early construct mechanical
function implies a need for new tissue engineering technologies
such as scaffolds and bioreactors [13], [14]. For example, the
stiffness and strength of previously used scaffolds were several
orders of magnitude below normal articular cartilage, particularly
in tension [12], [15], [16]. Likewise, mechanical properties of
engineered cartilage produced using these scaffolds and hMSC
were at least one order of magnitude below values reported for
normal cartilage despite prolonged in vitro culture [17], [18].
In Vitro Generation of Mechanically Functional Cartilage Grafts . . .
The goal of the present study was to produce mechanically
functional tissue engineered cartilage from adult hMSC and
3D-woven PCL scaffolds in 21 days in vitro. Effects of (1)
scaffold structure (loosely vs. tightly woven PCL); (2) culture
vessel (static dish vs. oscillating bioreactor); and (3) medium
composition (chondrogenic additives with or without serum) on
construct mechanical, biochemical, and molecular properties
were quantified. A 3D weaving method [19] was applied to
multifilament PCL yarn to create scaffolds with cartilage-mimetic
mechanical properties. The PCL was selected because it is a
FDA-approved, biocompatible material [20], [21] that supports
chondrogenesis [22] and degrades slowly (i.e., less than 5%
degradation at 2 years, as measured by mass loss) into byproducts
that are entirely cleared from the body [23], [24].
The 3D-woven PCL scaffolds were seeded with hMSC mixed
with Matrigel® such that gel entrapment enhanced cell seeding
efficiency [25] and also helped to maintain spherical cell
morphology for the promotion of chondrogenesis [26]. The
hMSC-PCL constructs were cultured either in static dishes or in an
oscillatory bioreactor that provided bidirectional percolation of
culture medium directly through the construct [27]. Bioreactors
were studied because these devices are known to enable functional
tissue engineering due to the combined effects of enhanced mass
transport and mechanotransduction [14], [28]-[34]. Bidirectional
rather than unidirectional perfusion was selected because the
latter yielded different conditions at opposing construct upper
and lower surfaces resulting in spatial concentration gradients
and inhomogeneous tissue development [35], [36].
21

Three different culture media were tested as follows. Differentiat-
ion medium 1 (DM1) containing serum and chondrogenic
additives (TGFβ, ITS+ Premix, dexamethasone, ascorbic acid,
proline, and nonessential amino acids) was selected based on
our previous work [8], [17], [37]. Differentiation medium 2
(DM2) containing chondrogenic additives but not serum was
selected based on reports that serum inhibited chondrogenesis by
synoviocytes [38], [39] and caused hypertrophy of chondrocytes
[40]. Control medium (CM) without chondrogenic additives was
tested to assess spontaneous chondrogenic differentiation in
hMSC-PCL constructs.
Materials and Methods
All tissue culture reagents were from Invitrogen (Carlsbad, CA)
unless otherwise specified.
Poly(ε-caprolactone) (PCL) Scaffolds
A custom-built loom [19] was used to weave PCL multifilament
yarns (24 µm diameter per filament; 44 filaments/yarn, Grilon
KE-60, EMS/Griltech, Domat, Switzerland) in three orthogonal
directions (x-warp, y-weft, and a vertical z-direction) (Figure 1A).
A loosely woven scaffold was made with widely spaced warp yarns
(8 yarns/cm), closely spaced weft yarns (20 yarns/cm), and two
z-layers between each warp yarn (Figure 1B). A tightly woven
scaffold was made with closely spaced warp and weft yarns (24
and 20 yarns/cm, respectively) and one z-layer between each warp
yarn (Figure 1C). These weaving parameters, in conjunction with
fiber size and the density of PCL (1.145 g/cm3 ) [41], determined
scaffold porosity and pore dimensions. The loosely woven scaffold
had porosity of 68 ± 0.3%, approximate pore dimensions of 850
µm × 1100 µm × 100 µm, and approximate thickness of 0.9 mm.
The tightly woven scaffold had porosity of 61 ± 0.2%, approximate
pore dimensions of 330 µm × 260 µm × 100 µm and approximate
thickness of 1.3 mm. Prior to cell culture, scaffolds were immersed
in 4 N NaOH for 18 h, thoroughly rinsed in deionized water, dried,
ethylene oxide sterilized, and punched into 7-mm diameter discs
using dermal punches (Acuderm Inc., Ft. Lauderdale, FL).
Human Mesenchymal Stem Cells
The hMSC were derived from bone marrow aspirates obtained
from a healthy middle-aged adult male at the Hematopoietic Stem
Cell Core Facility at Case Western Reserve University. Informed
consent was obtained, and an Institutional Review Board-approved
aspiration procedure was used [42]. Briefly, the bone marrow
Figure 1. The 3D-woven PCL scaffold. (A) schematic; (B-C)
scanning electron micrographs of (B) loosely and (C) tightly woven
scaffolds. Scale bars: 1 mm.
22
In Vitro Generation of Mechanically Functional Cartilage Grafts . . .
sample was washed with Dulbecco’s modified Eagle’s medium
(DMEM-LG, Gibco) supplemented with 10% fetal bovine serum
(FBS) from a selected lot [9]. The sample was centrifuged at 460×g
on a preformed Percoll density gradient (1.073 g/mL) to isolate
the mononucleated cells. These cells were resuspended in serumsupplemented
medium and seeded at a density of 1.8 × 105 cells/
cm2 in 10-cm diameter plates. Nonadherent cells were removed
after 4 days by changing the medium. For the remainder of the cell
expansion phase, the medium was additionally supplemented with
10 ng/mL of recombinant human fibroblast growth factor-basic
(rhFGF-2, Peprotech, Rocky Hill, NJ) [43], and was replaced twice
per week. The primary culture was trypsinized after approximately
2 weeks, and then cryopreserved using Gibco Freezing medium.
Tissue Engineered Constructs
The hMSC were thawed and expanded by approximately 10-fold
during a single passage in which cells were plated at 5500 cells/
cm2 and cultured in DMEM-LG supplemented with 10% FBS, 10
ng/mL of rhFGF-2, and 1% penicillin-streptomycin-fungizone.
Medium was completely replaced every 2-3 days for 7 days.
Multipotentiality was verified for the expanded hMSC by inducing
differentiation into the chondrogenic lineage in pellet cultures
of passage 2 (P2) cells [44] and into adipogenic and osteogenic
lineages in monolayer culture [45]. The PCL scaffolds (a total of
n = 15-20 per group, in three independent studies) were seeded
with P2 hMSC by mixing cells in growth factor-reduced Matrigel®
(B&D Biosciences) while working at 4°C, and pipetting the cellgel
mixture evenly onto both surfaces of the PCL scaffold. Each
7 mm diameter, 0.9 mm thick loosely woven scaffold was seeded
with a cell pellet (1 million cells in 10 µL) mixed with 25 µL of
Matrigel®, whereas each 7 mm diameter, 1.3 mm thick tightly
woven scaffold was seeded with a similar cell pellet mixed with 35
µL of Matrigel®. Freshly seeded constructs were placed in 24-well
plates (one construct per well), placed in a 37°C in a humidified,
5% CO /room air incubator for 30 min to allow Matrigel® gelation,
2
and then 1 mL of medium was added to each well.
After 24 h, constructs were transferred either into 6-well plates
(one construct per well containing 9 mL of medium) and cultured
statically, or into bioreactor chambers as described previously
[27]. Briefly, each construct allocated to the bioreactor group
was placed in a custom-built poly(dimethyl-siloxane) (PDMS)
chamber that was connected to a loop of gas-permeable silicone
rubber tubing (1/32-in wall thickness, Cole Parmer, Vernon
Hills, IL). Each loop was then mounted on a supporting disc, and
medium (9 mL) was added, such that the construct was submerged
in medium in the lower portion of the loop and a gas bubble was
present in the upper portion of the loop [27]. Multiple loops were
mounted on an incubator-compatible base that slowly oscillated
the chamber about an arc of ~160 deg. Importantly, bioreactor
oscillation directly applied bidirectional medium percolation and
mechanical stimulation to their upper and lower surfaces of the
discoid constructs.
Three different medium compositions (DM1, DM2 and
CM) were studied. Differentiation medium 1 (DM1) was
DMEM-HG supplemented with 10% FBS, 10 ng/mL hTGFβ-3
(PeproTech, Rocky Hill, NJ), 1% ITS+ Premix (B&D Biosciences),

10-7 M dexamethasone (Sigma), 50 mg/L ascorbic acid, 0.4 mM
proline, 0.1 mM nonessential amino acids, 10 mM HEPES, 100
U/mL penicillin, 100 U/mL streptomycin, and 0.25 μg/mL of
fungizone. Differentiation medium 2 (DM2) was identical to DM1
except without FBS. Control medium (CM) was identical to DM1
except without chondrogenic additives (TGFβ-3, ITS+ Premix, and
dexamethasone). Media were replaced at a rate of 50% every 3-4
days, and constructs were harvested after 1, 7, 14, and 21 days.
Mechanical Testing
Confined compression tests [46] were performed on 3-mm
diameter cylindrical test specimens, cored from the centers of
21-day constructs or acellular (initial) scaffolds, using an ELF-
3200 materials testing system (Bose-Enduratec, Framingham,
MA). Specimens (n = 5-6 per group) placed in a 3 mm diameter
confining chamber within a bath of phosphate buffered saline
(PBS), and compressive loads were applied using a solid piston
against a rigid porous platen (porosity of 50%, pore size of 50-
100 μm). Following equilibration of a 10 gf tare load, a step
compressive load of 30 gf was applied to the sample and allowed
to equilibrate for 2000 s. Aggregate modulus (H ) and hydraulic
A
permeability (k) were determined numerically by matching the
solution for axial strain (ε ) to the experimental data for all creep
z
tests using a two-parameter, nonlinear least-squares regression
procedure [47], [48]. Unconfined compression tests were done by
applying strains, ε, of 0.04, 0.08, 0.12, and 0.16 to the specimens
(n = 5-6 per group) in a PBS bath after equilibration of a 2% tare
strain. Strain steps were held constant for 900 s, which allowed
the specimens to relax to an equilibrium level. Young’s modulus
was determined by linear regression on the resulting equilibrium
stress-strain plot.
Histology and Immunohistochemistry
Histological analyses were performed after specimens (n = 2
constructs per time point per group) were fixed in 10% neutral
buffered formalin for 24 h at 4°C, post fixed in 70% ethanol,
embedded in paraffin, and sectioned both en-face and in cross
section. Sections 5 μm thick were stained with safranin-O/fast
green for proteoglycans. For immunohistochemical analysis, 20
μm thick sections were deparaffinized in xylene and rehydrated.
To efficiently expose epitopes, the sections were incubated with
700 U/mL bovine testicular hyaluronidase (Sigma) and 2 U/mL
pronase XIV (Sigma) for 1 h at 37°C. Double immunostaining for
collagen type I (mouse monoclonal antibody, ab6308, Abcam Inc.,
Cambridge, MA) and collagen type II (mouse monoclonal antibody,
CII/CI, Hybridoma Bank, University of Iowa) was performed using
Table 1. The Sequence of PCR Primers (Sense and Antisense, 5’ to 3’).
In Vitro Generation of Mechanically Functional Cartilage Grafts . . .
an Avidin/Biotin kit (Vector Lab, Burlingame, CA). Control sections
were incubated with PBS/1% bovine serum albumin (Sigma)
without primary antibody.
Biochemical Analyses
Standard assays for DNA, ortho-hydroxyproline (OHP, an index
of total collagen content), and GAG (an index of proteoglycan
content) were performed (n = 3-4 bisected constructs per time
point per group). Values obtained for all 1-day constructs produced
from tightly woven PCL scaffolds in DM1, DM2, and CM groups
were pooled, averaged, and used as a basis for comparison for
subsequent (7, 14, and 21-day) constructs produced from tightly
woven scaffolds. After measuring wet weight, constructs were
diced and digested in papain for 12 h at 60°C. DNA was measured
using the Quant-iTTM PicoGreen® dsDNA assay (Molecular Probes,
Eugene, OR). GAG was measured using the BlyscanTM sulphated GAG
assay (Biocolor, Carrickfergus, Northern Ireland). To measure total
collagen, papain digests were hydrolyzed in HCl at 110°C overnight,
dried in a desiccating chamber, reconstituted in acetate-citrate
buffer, filtered through activated charcoal, and OHP was quantified
by Ehrlich’s reaction [49]. Briefly, hydrolysates were oxidized with
chloramine-T, treated with dimethylaminobenzaldehyde, read at
540 nm against a trans-4-hydroxy-L-proline standard curve, and
total collagen was calculated by using a ratio of 10 μg of collagen
per 1 μg of 4-hydroxyproline. The conversion factor of 10 was
selected since immunohistochemical staining showed that type
II collagen represented virtually all of the collagen present in the
constructs [50], [51].
Reverse Transcriptase Polymerase Chain Reaction (RT-PCR)
The presence of two cartilage biomarkers was tested: Sox-9,
one of the earliest markers for MSC differentiation toward the
chondrocytic lineage, preceding the activation of collagen II [52],
and collagen type II, a chondrocyte-related gene. Collagen type I
provided a marker for undifferentiated MSC, and GAPDH provided
an intrinsic control [53]. Total RNA was isolated from hMSC prior to
and after culture on PCL scaffolds (n = 3-4 bisected constructs per
group per time point) using a Qiagen RNeasy mini kit. DNase treated
RNA was used to make first stranded cDNA with the SuperScript
III First-Strand Synthesis for RT-PCR. The cDNA was amplified in
an iCycler Thermal Cycler 582BR (Bio-Rad, Hercules, CA) using
primer sequences given in Table 1. The cycling conditions were as
follows: 2 min at 94°C; 30 cycles of (30 s at 94°C, 45 s at 58°C, 1
min at 72°C), and 5 min at 72°C. The PCR products were analyzed
by means of 2% agarose gel electrophoresis containing ethidium
bromide (E-Gel® 2%, Invitrogen).
Primer Sense Antisense Product size
Collagen type II (Col II) atgattcgcctcggggctcc tcccaggttctccatctctg 260 bp
Sox-9 aatctcctggaccccttcat gtcctcctcgctctccttct 198 bp
Collagen type I (Col I) gcatggccaagaagacatcc cctcgggtttccacgtctc 300 bp
23

Statistical Analysis
Data were calculated as mean ± standard error and analyzed
using multiway analysis of variance (ANOVA) in conjunction with
Tukey’s post hoc test using Statistica (v. 7, Tulsa, OK, USA). Values
of p < 0.05 were considered statistically significant.
Results
Effects of Scaffold Structure
Scaffold structure did not have any significant effect on the
amounts of DNA, total collagen, or GAG in constructs cultured
statically for 21 days in DM1 (Table 2, Group A vs. B). In contrast,
scaffold structure significantly impacted aggregate modulus
(H ) and Young’s modulus (E) of initial (acellular) scaffolds and
A
cultured constructs (Figure 2). Acellular loosely woven scaffolds
exhibited lower (p < 0.05) mechanical properties (H of 0.18 ±
A
0.011 MPa and E of 0.042 ± 0.004 MPa) than acellular tightly
woven scaffolds (H of 0.46 ± 0.049 MPa and E of 0.27 ± 0.017
A
MPa). Likewise, 21-day constructs based on loosely woven
scaffolds exhibited lower (p < 0.05) mechanical properties (H of A
0.16 ± 0.006 MPa and E of 0.064 ± 0.004 MPa) than constructs
based on tightly woven scaffolds (H of 0.37 ± 0.030 MPa and E of
A
0.41 ± 0.023 MPa) (Figure 2). As compared to acellular scaffolds,
the 21-day constructs exhibited similar aggregate modulus and
higher (p < 0.05) Young’s modulus.
Effects of Culture Vessel
Aggregate modulus of 21-day constructs based on tightly woven
PCL and cultured in bioreactors was higher (p < 0.05) than that
measured for otherwise similar constructs cultured statically
(Figure 2, Table 2). Construct amounts of DNA, total collagen, and
24
0.8
0.6
0.4
0.2
0.0
In Vitro Generation of Mechanically Functional Cartilage Grafts . . .
GAG increased in a manner dependent on time, culture vessel,
and medium composition. DNA and GAG contents were similar in
21-day constructs cultured in bioreactors and statically (Figure
3A and C). Total collagen content was 1.5-fold higher (p < 0.05)
in bioreactors compared to static cultures (Figure 3B; Table 2,
Group B vs. C). Bioreactors yielded more homogeneous tissue
development than static cultures based on qualitative histological
appearance of cross sections (Figure 4, row I). Chondrogenesis
was demonstrated histologically by rounded cells within a hyalinelike
matrix that immunostained strongly and homogeneously
positive for collagen type II. Bioreactors yielded constructs in
which Coll-II immunostaining was more pronounced than static
cultures (Figure 4, row IV). Immunostaining for Col I was minimal
under all conditions tested. The RT-PCR analysis showed type of
culture vessel did not affect the temporal expression of mRNAs for
collagen type II (Figure 5A), Sox-9 (Figure 5B), collagen type I (not
shown), and GAPDH (not shown).
Effects of Medium Composition
DNA content was 1.4-fold higher (p < 0.05) in 21-day constructs
cultured in DM1 compared to DM2 (Figure 3A, Table 2, Group B
vs. D). Also, total collagen content was 1.8-fold higher (p < 0.05)
in 21-day constructs cultured in DM1 compared to DM2 (Figure
3B). Conversely, GAG content was lower (42% as high, p < 0.05)
in 21-day constructs cultured in DM1 compared to DM2 (Figure
3C). Likewise, the GAG/DNA ratio was lower (30% as high, p

Table 2. Mechanical and Biochemical Properties of hMSC-PCL Constructs after Short-Term Culture.
Parameter Culture
time
(days)
Aggregate Modulus
(H , MPa, n = 5-6)
A
Young’s Modulus
(E, MPa, n = 5-6)
DNA
(μg/construct, n = 3-4)
Collagen
(μg/construct, n = 3-4)
Collagen per DNA
(mg/mg, n = 3-4)
Glycosaminoglycans
(GAG, μg/construct, n
= 3-4)
GAG per DNA
(mg/mg, n = 3-4)
Construct wet weight
(mg/construct, n =
3-4)
Group A Group B Group C Group D Group E
Loosely woven
PCL Static, in
DM1 AVG ± SEM
Tightly woven
PCL Static, in
DM1 AVG ± SEM
In Vitro Generation of Mechanically Functional Cartilage Grafts . . .
Tightly woven
PCL Bioreactor, in
DM1 AVG ± SEM
Tightly woven
PCL Static, in
DM2 AVG ± SEM
21 0.16 ± 0.006 0.37 ± 0.03 a 0.55 ± 0.084 b NM NM
21 0.06 ± 0.004 0.41 ± 0.023 a 0.34 ± 0.0099 NM NM
1
7
14
21
1
7
14
21
1
7
14
21
1
7
14
21
1
7
14
21
1
7
14
21
4.76 ± 0.28
10.6 ± 0.82
11.1 ± 0.29
9.15 ± 0.31
57.5 ± 2.66
182 ± 18.40
295 ± 0.37
413 ± 6.66
12.2 ± 1.28
17.2 ± 1.21
26.5 ± 0.69
45.3 ± 1.41
6.72 ± 1.26
52.7 ± 1.55
80.0 ± 4.72
110 ± 4.62
1.40 ± 0.18
5.03 ± 0.26
7.17 ± 0.38
12.1 ± 0.78
37.7 ± 2.10
46.8 ± 0.59
55.3 ± 1.20
54.7 ± 0.80
4.74 ± 0.15
9.02 ± 0.65
10.1 ± 0.43
10.7 ± 0.74
23.5 ± 9.74
124 ± 16.8
274 ± 17.5
395 ± 4.64
5.23 ± 2.34
14.2 ± 2.61
27.2 ± 1.33
37.6 ± 2.53
7.78 ± 0.63
48.2 ± 1.49
85.0 ± 4.43
138 ± 9.13
1.65 ± 0.15
5.41 ± 0.33
8.49 ± 0.51
13.3 ± 1.75
62.7 ± 0.67
67.9 ± 0.98
72.7 ± 2.16
74.9 ± 0.82
4.74 ± 0.15
8.47 ± 0.25
12.7 ± 0.85b 11.5 ± 0.72
23.5 ± 9.74
55.2 ± 6.52b 501 ± 35.1b 585 ± 66.5b 5.23 ± 2.34
6.48 ± 0.58b 40.1 ± 4.73b 52.0 ± 8.98
7.78 ± 0.63
26.4 ± 2.97b 91.9 ± 5.83
140 ± 7.94
1.65 ± 0.15
3.11 ± 0.29b 7.32 ± 0.70
12.4 ± 1.23
62.7 ± 0.67
57.9 ± 1.56
64.2 ± 2.04
65.2 ± 1.08
4.74 ± 0.15
6.16 ± 0.50
6.63 ± 0.44c 7.70 ± 0.38c 23.5 ± 9.74
26.1 ± 7.20c 125 ± 21.4c 219 ± 32.6c 5.23 ± 2.34
4.13 ± 0.90c 18.7 ± 2.43
28.3 ± 3.06
7.78 ± 0.63
32.1 ± 4.85
149 ± 22.6c 326 ± 47.7c 1.65 ± 0.15
5.14 ± 0.44
22.4 ± 2.45c 41.9 ± 4.00c 62.7 ± 0.67
62.6 ± 2.67
64.8 ± 1.54
69.7 ± 1.72
Tightly woven
PCL Static, in CM
AVG ± SEM
4.74 ± 0.15
NM
NM
6.14 ± 0.237d 23.5 ± 9.74d NM
NM
105 ± 18.4d 5.23 ± 2.34
NM
NM
17.1 ± 244d 7.78 ± 0.63
NM
NM
20.4 ± 1.28d 1.65 ± 0.15
NM
NM
3.32 ± 0.18d 62.7 ± 0.67
NM
NM
71.4 ± 0.38
Static = culture in petri dish; Bioreactor = culture in gas-permeable loop with slow, bidirectional oscillation; DM1 = differentiation medium #1; DM2 = differentiation
medium #2; CM = control medium; n = number of samples tested; NM = not measured.
Multiway ANOVA for Groups A-C for the culture time of 21 days showed significant effects of scaffold structure and culture vessel.
Multiway ANOVA for Groups A-D for culture times of 1-21 days showed significant effects of time, culture vessel, and culture medium composition.
aSignificant effect due to scaffold structure.
bSignificant effect due to culture vessel.
cSignificant effect due to presence of FBS in culture medium.
dSignificant effect due to presence of chondrogenic additives in culture medium.
25

A
B
C
positive matrix staining for GAG and Coll-II, Figure 4, rows III and
IV). Culture in DM2 yielded earlier expression of collagen type
II mRNA (Figure 5A) such that this biomarker was present by
day 7 in contrast to DM1. Constructs cultured in CM contained
substantially lower amounts of collagen and GAG compared to
DM1 (Table 2, Group B vs. E). Chondrogenic differentiation was
virtually absent in constructs cultured in CM with respect to
rounded cell shape, matrix staining for GAG and collagen type II,
and measured GAG and collagen contents (Figures 3 and 4).
26
DNA (μg/sample)
Collagen (μg/sample)
GAG (μg/sample)
15
10
5
600
500
400
300
200
100
0
1d 7d 14d 21d
Static dish
DM1
400
300
200
100
0
1d 7d 14d 21d
Static dish
DM1
0
1d 7d 14d 21d
Static dish
DM1
a
1d 7d 14d 21d 1d 7d 14d 21d 21d
Bioreactor
DM1
a
Bioreactor
DM1
a
1d 7d 14d 21d 1d 7d 14d 21d 21d
Bioreactor
DM1
b
Static dish
DM2
b
Static dish
DM2
In Vitro Generation of Mechanically Functional Cartilage Grafts . . .
b
b
c
Static
CM
1d 7d 14d 21d 1d 7d 14d 21d 21d
b
Static dish
DM2
b
c
Static
CM
c
Static
CM
Figure 3. Amounts of (A) DnA, (B) total collagen, and (C)
glycosaminoglycans (GAG) in constructs produced from tightly
woven scaffolds and hMSC cultured for up to 21 days, statically
or in bioreactors in DM1, statically in DM2, and statically
in CM. aSignificant difference due to type of culture vessel,
b c Significant difference due to serum, Significant difference due to
chondrogenic additives.
I
II
III
IV
Static dish,
DM1
Bioreactor,
DM1
Static dish,
DM2
Static Dish,
CM
Figure 4. Histological appearance of constructs produced from
tightly woven scaffolds and hMSC cultured for 21 days statically
in DM1 (column 1), in bioreactors in DM1 (column 2), statically
in DM2 (column 3) or statically in CM (column 4). Rows I-II:
full cross-section (I) or en-face (II-III) sections stained with
safranin-O/fast green (GAG appears orange-red, cell nuclei
black, and PCL scaffold white); Row IV: en-face sections double
immunostained for collagen types I and II (Coll-II appears green,
Coll-I was stained red and was not seen, DAPI-counterstained cell
nuclei appear blue). Scale bars: 200 μm (Rows I and II); 20 μm
(Row III); 100 μm (Row IV).
Figure 5. Electrophoresis gels for RT-PCR products of collagen
type II and of Sox-9. Lane 1 = day 0 hMSCs; Lanes 2 and 3 = 7 days
and 21 days static culture in DM1; Lanes 4 and 5 = 7 days and 21
days bioreactor culture in DM1; Lanes 6 and 7 = 7 days and 21 days
static culture in DM2; Lane 8 = control for DnA contamination.
The DnA Ladder shown is TrackItT 100 bp.
Discussion
The findings of this study demonstrate the ability to produce
functional tissue engineered cartilage starting from hMSC and a
tightly woven PCL scaffold within 21 days in vitro. Importantly,
aggregate and Young’s moduli of hMSC-PCL constructs cultured
statically and in bioreactors (Table 2, Groups B and C), approached
the values reported for normal articular cartilage (H A of 0.1-2.0
MPa; E of 0.4-0.8 MPa) [54]-[56]. Young’s modulus was higher
for 21-day constructs than initial acellular scaffolds, which may
be due to accumulation of cell-derived cartilaginous extracellular
matrix within the 3D-woven scaffold and associated increase
in shear modulus. These effects would be expected to reduce
relative PCL yarn movement and cross-sectional shape distortion

during compressive testing and have a more pronounced effect
during mechanical testing in the unconfined configuration (i.e.,
where scaffolds are not laterally constrained) than the confined
configuration, thereby affecting E more than H . Although short-
A
term maintenance of mechanical properties of constructs and
scaffolds was demonstrated, further studies of constructs and
acellular scaffolds are warranted to assess mechanical properties
over longer time periods. Long-term maintenance can be
reasonably expected due to the slow biodegradation of PCL [23],
[24] in concert with continued accumulation of cell-derived
cartilaginous matrix.
Efficient hMSC seeding and cartilaginous matrix deposition
were observed for loosely and tightly woven PCL scaffolds and
can be attributed to the combination of Matrigel®-enhanced cell
entrapment [25] and large, homogenously distributed pores
in the scaffold (i.e., in-plane pores of 250-1000 μm, Figure 1).
Consistently, scaffolds with 250-500 μm pores were found to
enhance GAG secretion as compared to smaller pores [57],
and 380-405 μm pores were found suitable for chondrocyte
proliferation [58]. Culture in an oscillating bioreactor yielded
constructs with higher aggregate modulus, higher total collagen
content, and more homogeneous tissue development, especially
at the upper and lower surfaces than otherwise identical static
cultures (Figures 2-4). Constructs from the oscillating bioreactor
exhibited strongly positive immunostaining for Coll-II and
virtually negative staining for Coll-I, although collagen type II
as a fraction of the total collagen was not measured explicitly.
We previously showed that constructs from rotating bioreactors
contained more total and type II collagen than statically cultured
controls [12], and in these constructs, Coll-II represented 92-
99% of the total collagen [51], [59]. Consistently, others showed
hydrodynamic shear increased total collagen, type II collagen,
and tensile modulus of multilayered chondrocyte sheets [60],
and bidirectional perfusion yielded more homogeneous tissue
engineered cartilage than unidirectional perfusion [35], [36].
Culture medium composition significantly impacted construct
amounts of DNA and GAG, intensity of safranin-O staining and
Coll- II immunostaining, and the temporal profile of chondrogenic
differentiation by hMSC. Specifically, chondrogenic additives
without serum (DM2) yielded constructs with higher GAG,
higher GAG/DNA ratio, earlier expression of collagen II mRNA,
and more homogenous immunostaining for Coll-II as compared
to chondrogenic additives with serum (DM1) (Figures 3-5).
Consistently, serum was recently reported to inhibit chondrogenic
differentiation of synoviocytes [38], [39], and this finding was
attributed to an enhanced proliferation of the cells that hindered
their differentiation capacity. Interestingly, the GAG/DNA ratio
measured in the present study at culture day 21 exceeded the
GAG/DNA ratios previously reported after prolonged in vitro
cultivation [17], [39], [61].
Conclusion
In this work, a 3D-woven PCL scaffold combined with adult human
stem cells yielded mechanically functional tissue engineered
cartilage constructs within only 21 days in vitro. Scaffold structure
significantly impacted construct aggregate and Young’s moduli
In Vitro Generation of Mechanically Functional Cartilage Grafts . . .
(i.e., tightly woven scaffolds yielded constructs with higher moduli
than more loosely woven scaffolds). Importantly, compressive
moduli of 21-day constructs based on tightly woven scaffolds
approached values reported for normal articular cartilage.
Production of constructs with robust mechanical properties was
accelerated by culture in oscillating bioreactors as compared to
static dishes (i.e., the bioreactor yielded constructs with higher H , A
higher total collagen content, more immunostaining for collagen
type II, and more spatially homogenous tissue development).
Chondrogenic differentiation of hMSC was observed only if
culture medium was supplemented with chondrogenic additives
(TGFβ, ITS+ Premix), and was accelerated if this medium did not
contain serum (i.e., lack of serum yielded constructs with higher
GAG content, higher GAG/DNA ratio, earlier expression of collagen
type II mRNA, and more pronounced matrix staining for GAG and
collagen type II).
Acknowledgments
This work was supported by the Academy of Finland and the
Finnish Cultural Foundation (PKV), NIH AR055414-01 (LEF),
NASA NNJ04HC72G (LEF), NIH AR050208 (JFW), NIH P01
AR053622 (AIC, JFW), and NIH AR48852 (FG). We thank EMS/
Griltech (Domat, Switzerland) for donating the multifilament PCL
yarn, A. Gallant for expert help with the oscillating bioreactor, and
C.M. Weaver for help with manuscript preparation. One of the
authors (FG) owns equity in Cytex Therapeutics, Inc. The other
authors have no known conflicts of interest associated with this
publication.
Notes
Figures with essential color discrimination, Figures 1, 2, and 4 in
this article, are difficult to interpret in black and white. The full
color images can be found in the on-line version, at doi:10.1016/j.
biomaterials.2009.11.092.
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29

30
Piia K. Valonen is currently a Postdoctoral Researcher at the University of Eastern Finland. Her research is
focused on Alzheimer’s disease, concentrating on how different cell types (e.g., mesenchymal stem cells) differ
between healthy and AD patients. She is also involved in a Clean Room Training Center project. In November
2007, she joined Dr. Lisa Freed’s group at the Harvard-MIT Division of Health Sciences & Technology as a
Postdoctoral Associate for 13 months. Dr. Valonen holds M.Sc. and Ph.D. degrees from the University of Kuopio
(now the University of Eastern Finland).
Franklin T. Moutos is currently a Research Scholar at Duke University Medical Center and is working to develop
new technologies for the treatment of degenerative joint diseases. He has broad experience in the application of
textile and biomedical engineering principles to the development of biomaterial scaffolds for tissue regeneration.
Prior to completing his graduate studies, he spent three years in a startup company that designed and produced
high-performance textiles and composite materials for aerospace, industrial, and biomedical applications. Dr.
Moutos received B.S. and M.S. degrees in Textile Material Science from North Carolina State University and a Ph.D.
in Biomedical Engineering from Duke University.
Akihiko Kusanagi is an Assistant Professor at Shiga University of Medical Science in Japan. Previously, he was a
Postdoctoral Fellow at the Langer Research Laboratory, MIT (2006-2010). He has extensive background in cartilage
tissue engineering and stem cell research, including human embryonic stem cells, iPS cells, and mesenchymal
stem cells. Prior to joining to MIT, he was Director of Research and Development at the Histogenics Corporation, a
leading company for cartilage tissue engineering for clinical applications. He was in charge of several orthopedicbased
tissue engineering technologies, and one product, NeoCart, is in an FDA phase III clinical trial in the U.S. Dr.
Kusanagi earned B.S. and D.V.M. degrees from Azabu University and a Ph.D. in Veterinary Medicine from University
of Tokyo.
Matteo G. Moretti is head of the Cell and Tissue Engineering Laboratory at IRCCS Galeazzi Orthopaedic
Institute, Milan, Italy. His main research interests are osteochondral and cardiovascular tissue engineering and
multiscale bioreactor systems aimed at developing microfluidic and traditional tissue bioreactor technologies as
a key to more viable and accessible tissue and cell therapies. He holds B.Eng and M.Sc degrees in Bioengineering
from Politecnico di Milano and Trinity College Dublin, respectively. He carried out part of his doctoral studies in
Prof. I. Martin’s Lab at Basel University and obtained a Ph.D. from Politecnico di Milano. Dr. Moretti then worked as
Postdoctoral Fellow at the Harvard-MIT Division of Health Science and Technology supervised by Dr. Lisa Freed.
Brian O. Diekman is a Ph.D. student in Biomedical Engineering at Duke University. His research investigates
human and mouse stem cell populations for use in cellular therapy and cartilage tissue engineering. He was
awarded a Fulbright Student Grant to perform stem cell research at the Regenerative Medicine Institute in Galway,
Ireland. Mr. Diekman earned a B.S. in Biomedical Engineering from Duke University.
In Vitro Generation of Mechanically Functional Cartilage Grafts . . .

Jean F. Welter is a Research Associate Professor at the Skeletal Research Center, Department of Biology, Case
Western Reserve University, Cleveland, OH. Current research interests include tissue engineering, primarily of
cartilage, but also bone and skin; quality control of mesenchymal stem cells and tissue engineered products,
focusing on nondestructive testing methods for the latter; bioreactor design and development; bone grafting;
and intra-articular drug delivery for OA/RA. He has published 32 papers, 6 book chapters, and has one patent
application pending. Dr. Welter holds a Doktor der gesamten Heilkunde (M.D.) from the School of Medicine,
Leopold Franzens Universität, Innsbruck, Austria, an M.Sc. in Experimental Surgery from McGill University,
Montréal, Québec, Canada, and a Ph.D. in Physiology and Biophysics from Case Western Reserve University,
Cleveland, OH.
Arnold I. Caplan is Professor of Biology and the Director of the Skeletal Research Center at Case Western Reserve
University. His research has involved understanding the development, maturation, and aging and regeneration of
cartilage, bone, skin, and other mesenchymal tissues and pioneering research on mesenchymal stem cells (MSCs).
He and his collaborators have helped define the immunoregulatory and tropic activities of MSCs as manifested
by the secretion of a complex array of bioactive molecules at sites of tissue injury or inflammation. Dr. Caplan
received a B.S. in Chemistry at the Illinois Institute of Technology and a Ph.D. from The Johns Hopkins University
School of Medicine. Dr. Caplan did a Postdoctoral Fellowship in the Department of Anatomy at The Johns Hopkins
University, followed by Postdoctoral Fellowships at Brandeis University.
Farshid Guilak is the Laszlo Ormandy Professor of Orthopaedic Surgery and Director of Orthopaedic Research
at Duke University Medical Center. Dr. Guilak’s research focuses on the study of osteoarthritis, a painful and
debilitating disease of the synovial joints. His laboratory has used a multidisciplinary approach to investigate the
role of biomechanical factors in the onset and progression of osteoarthritis, as well as the development of new
tissue engineering therapies for this disease. His work in this area has focused on the use of adult stem cells in
combination with novel 3D biomaterial scaffolds for the regeneration of articular cartilage to treat osteoarthritis.
He received a B.S. in Biomedical Engineering from Rensselaer Polytechnic Institute, and a Ph.D. in Mechanical
Engineering from Columbia University.
Lisa E. Freed is a Senior Member of the Technical Staff in the Biomedical Engineering Group at Draper Laboratory
and an MIT-Affiliated Research Scientist in the Harvard-MIT Division of Health Sciences and Technology. She
has been a Principal Investigator at MIT since 1993 and at Draper since 2009. Dr. Freed’s research focuses on
the development and implementation of novel tools for tissue engineering, including biomaterial scaffolds with
tissue-mimetic properties and cell culture bioreactors that apply physiologic biophysical stimuli to accelerate
tissue growth. With collaborators, she has engineered functional tissues that resemble heart muscle and cartilage
(i.e., muscle that withstands tensile loading and propagates electrical signals and skeletal tissue constructs that
withstand compressive loading). She received a S.B. in Biology from MIT, a Ph.D. in Applied Biological Sciences
from MIT, and the M.D. from Harvard Medical School.
In Vitro Generation of Mechanically Functional Cartilage Grafts . . .
31

32
r s
Most vehicles can be refueled repeatedly over the course of their
lifetime, but gas stations don’t exist in space today – or in the near
future – so a satellite is no longer useful once its tank is empty. Draper
engineers may solve this problem by replacing the fuel that runs the
spacecraft’s propulsion system – which keeps it in the correct position
as well as maneuvers when necessary – with magnetic forces.
In addition to enabling satellites to stay in place and maneuver
indefinitely, relying on the push from the Earth’s magnetic field would
enable far more frequent orbit changes, which today are only done if
absolutely necessary in order to conserve fuel.
Taking this approach would shake up the way that satellites have
traditionally been designed. Fuel takes up a significant portion of a
spacecraft’s mass today; that space would be filled by a larger power
system to harness the magnetic forces, which are less powerful than
rocket fuel, leading to slower, though unlimited, orbit changes.
while this approach could be used with any satellite in low Earth orbit,
it would be particularly useful for missions that benefit from repeated
orbit changes, including the Pentagon’s Operationally Responsive
Space Initiative.
L
General Bang-Bang Control Method for Lorentz Augmented Orbits
R
Illustration Credit: nASA

General Bang-Bang Control Method for Lorentz
Augmented Orbits
Brett J. Streetman and Mason A. Peck
Copyright © 2009 by Brett Streetman. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.
Abstract
An orbital control framework is developed for the Lorentz augmented orbit. A spacecraft carrying an electrostatic charge moves through
the geomagnetic field. The resulting Lorentz force is used in the general control framework to evolve the spacecraft’s orbit. The controller
operates with a high degree and order spherical-harmonic magnetic field model by partitioning the space of latitude in a meaningful way.
The partitioning reduces the complexity of the problem to a manageable level. A successful maneuver developed within this bang-off control
framework results in a combined orbital plane change and orbit raising. The cost of this maneuver is in electrical power. Reductions in the
power usage, at the expense of longer maneuver times, are obtained by using information about local plasma density.
Nomenclature
a = semimajor axis, m
B = Earth’s magnetic field, T
C = capacitance, F
E = specific energy, m/s
e = eccentricity
F L
= Lorentz force, N
h = specific angular momentum magnitude, m2 /s
h = specific angular momentum, m2 /s
i = inclination, rad
L = length of cylindrical capacitor, m
n^ = Earth spin axis
n e
= electron number density, 1/m 3
q = net spacecraft charge, C
q/m = charge-to-mass ratio, C/kg
R = stocking radius, m
r = radial coordinate, rad
r = spacecraft position, m
r = wire sheath radius, m
s
u = argument of latitude, rad
v = spacecraft velocity, m/s
ε 0
= permittivity of free space, F/m
θ = azimuth angle, rad
μ = gravitational parameter, m3 /s 2
ν = true anomaly, rad
φ = colatitude, rad
Ω = spacecraft right ascension of the ascending
node, rad
w = argument of perigee, rad
w = Earth’s spin rate, rad/s
E
Introduction
Propellantless propulsion opens up new possibilities for spacecraft
missions. One form of propellantless propulsion is the Lorentz
augmented orbit (LAO), which was first presented by Peck [1], and
General Bang-Bang Control Method for Lorentz Augmented Orbits
further examined through our later work [2], [3]. A body with net
charge q moving in a magnetic field B experiences a Lorentz force
F L = q(v – w E n ^ × r) × B (1)
where r is measured in an Earth-centered, inertial reference frame.
The velocity correction (-w n^ × r) is required because the magnetic
E
field B is constant only in an Earth-fixed frame. The charge-to-mass
ratio q/m of the spacecraft determines the magnitude of the Lorentz
acceleration. The direction of this acceleration is fixed by the
velocity of the spacecraft and the magnetic field at the spacecraft
location. Because the charge on the spacecraft can be maintained
solely with electrical power and because the Lorentz force acts
externally, LAO technology represents propellantless propulsion. If
q/m is varied as a control input, an LAO can achieve novel orbits and
enable new missions.
Orbit perturbations on charged particles due to the Lorentz force
have been observed in nature. Schaffer and Burns [4], [5] and
Hamilton [6] have studied these effects and derived various
perturbation equations. They have shown that the Lorentz force
acting on micron-sized, naturally charged dust grains creates
significant changes in their orbits. This effect explains features
seen in the ethereal rings of Jupiter. The dynamics of these charged
dust grains is well understood in the context of naturally occurring
systems.
Perturbations have also been examined in the context of the natural
charging of Earth-based spacecraft. Early studies include Sehnal
[7] in 1969. Others have tried to explain orbital deviations of
the LAGEOS spacecraft using naturally occurring Lorentz forces,
including work by Vokrouhlicky [8] and later Abdel-Aziz [9]. In
this work, we wish to not only examine the perturbations caused
by the Lorentz force, but to expand the available orbits and add
controlled charging to exploit the Lorentz dynamics for engineering
applications through LAOs.
33

Many other propellantless propulsion systems have been proposed.
The electrodynamic tether system is closely related to LAO. Tethers
force current through a long conductor [10]. The current in this
tether moving with the satellite creates a Lorentz force. By using
a current in a wire rather than a space charge on the spacecraft, a
tether can produce forces in directions an LAO spacecraft cannot.
However, the direction of the tether must be controlled, whereas
LAO is attitude-independent. LAO and tethers (along with other
propellantless propulsion systems) differ in from where they harvest
energy. LAO does work on a satellite by using the rotation of Earth’s
magnetic field. If in a perfect vacuum, an LAO system would require
only enough power to charge up and discharge the spacecraft.
A tether system is essentially a device for converting between
electrical energy and kinetic energy. Solar sails and magnetic sails
harvest energy from the sun to perform propellantless maneuvers.
In addition to LAO, a charged spacecraft architecture has been
proposed for formation flight [11], [12]. The Coulomb Spacecraft
Formation (CSF) concept makes use of the coulomb force acting
between two charged satellites, rather than the Lorentz force.
Whereas LAO uses an external force, CSF can produce only forces
internal to the formation. The CSF system performs better at higher
altitudes like geosynchronous earth orbit, whereas LAO produces
more useful formation forces in low Earth orbit (LEO) [13].
Our earlier studies [1]-[3] present the dynamics of LAOs under
simplified conditions, including greatly simplified magnetic field
models. This study expands that analysis to include spherical
harmonic magnetic fields of arbitrary complexity. The following
section, “Lorentz Perturbations,” gives an overview of the effect of
the Lorentz force on an orbit, drawn from previous work. The next
section, “Geomagnetic Field,” discusses the general properties of
the geomagnetic field. “Space-Vehicle Design” gives new material
on possible LAO system architectures. “Lorenz Augmented Orbit
Maneuvers and Limitations” presents a discussion of the maneuver
limitations introduced by the Lorentz force along with a compelling
mission enabled by the LAO concept. “Lorenz Augmented Orbit
Power Consumption and Plasma-Density-Based Control” considers
the effects of ionospheric conditions on performance and power
usage of an LAO spacecraft.
Lorentz Perturbations
The effects of the Lorentz force on an orbit are studied using
perturbation methods. We have previously shown that the change
in orbital energy E of a charged spacecraft affected by an arbitrary
magnetic field is given by [2]
34
Ė = w [(v . n E ^ )(B . r) – (v . r)(n^ q
. B)]
m
(2)
To describe this position r and velocity v, we use an Earth-centered,
inertial reference frame with spherical coordinates: radius r,
colatitude φ, and azimuth angle θ, as shown in Figure 1. In these
coordinates, Eq. (2) can be expressed as
Ė = w [(rv . n E ^ – cos φr . v)(B . r ^ ) + sin φ(r . v)(B . φ^ q
)]
m (3)
General Bang-Bang Control Method for Lorentz Augmented Orbits
The r^ and φ ^ unit vectors are shown in Figure 1.
n^
r^
φ
r
φ ^
Figure 1. Spherical coordinates and unit vectors used.
θ
Change in vector angular momentum is also found from
perturbation methods [2]:
h . = (B . r)v – (r . v)B – w (B . r)(n E ^ q
q
q
× r)
m m
m (4)
Equations (2) and (4) are used to obtain the derivatives of other
orbital elements. Equation (4) leads to an expression for the
derivative of inclination i [14]:
di
=
dt
h . cos i – h . . n ^
h sin i
In the spherical coordinates, Eq. (5) becomes
di –1
= Ė + w r E dt hw sin i E 2 (rv . n^ – cos φr . v) (B . r ^ q cos i
)
m h2 sin i
θ ^
(5)
+ (r . v)(B . φ^ ) + h sin i cos (θ – Ω)(r . v)(B . θ^ h cos i
)
sin φ (6)
The first term in Eq. (6) shows that changes in inclination are
coupled to changes in orbital energy, especially for orbits that are
near circular (where r . v goes to zero) or polar (where cos i goes
to zero). This coupling does not rise from some fundamental
relationship between energy change and inclination change,
but rather from the particulars of the Lorentz force. The energy
change is driven by the radial component of the magnetic field
and the apparent velocity induced by the rotation of the field. The
inclination change is generally driven by the radial component of
the magnetic field and the in-track velocity of the spacecraft. These

velocities, magnetic field components, and perturbation equations
happen to line up such that they depend on the same dynamic
quantities in the same relationships.
An expression for the change in eccentricity e is [14]
a
e
μ
. = (1 – e2 1/2
) (F . r L ^ ) sin ν
+ [F L . (h ^ . r ^ )] cos ν +
e + cos ν
1 + e cos ν (7)
This expression makes use of the Lorentz force F L explicitly.
Equations (3), (4), (6), and (7) are greatly simplified if we restrict
our discussion to circular (or near circular) orbits, where the term
(r . v) vanishes. Applying this simplification to Eq. (3) yields
Ė = w sin i cos u(B . r E ^ q μ
)
m r3 (8)
The argument of latitude is defined as the angle from the point of
right ascension of the ascending node (RAAN) to the spacecraft’s
position, measured around the orbit. Only the radial component of
the magnetic field affects the orbital energy of a circular LAO. If the
same simplification is applied to Eq. (4), we find that the change
in scalar angular momentum is simply a multiple of the change in
energy:
h . =
r
Ė
3
μ
The inclination change in a circular orbit follows the same pattern:
w r E
di cos i –1
= Ė
dt hw sin i E 2
h
General Bang-Bang Control Method for Lorentz Augmented Orbits
(9)
(10)
Equation (10) implies that, in circular orbits, orbital energy and
inclination are not independently controllable with the Lorentz
force. For every increase in energy, there is a corresponding
decrease in inclination. (This fact also holds true for any polar orbit,
eccentric or not.) This correlation limits the maneuvers that can be
performed with LAO-based propulsion.
The circular-orbit assumption simplifies Eq. (7), resulting in
q
e
m
. = 2 r2 sin i cos (θ – Ω) sin φ cos ν (B . r ^ h w a E
)
μ h μ
+ sin ν w r sin φ – (B . φ E ^ h cos i
)
r sin φ
– sin i cos (θ – Ω) sin ν(B . θ^ h
)
r
(11)
The change in eccentricity depends on all three components of the
magnetic field, making for more complicated analysis. Each term in
Eq. (11) involves the true anomaly ν. This relationship shows the
importance of radial velocity, which is also explicitly related to ν.
Changes in eccentricity are driven by small deviations from the
circular-orbit assumption.
Geomagnetic Field
The simplest model of the Earth’s magnetic field is a dipole aligned
with Earth’s spin axis. However, this simple model fails to describe
two important features for an LAO: that the dipole component
is not aligned with the Earth’s spin axis and that terms higher in
degree than the dipole are significant components of the field. The
Earth’s magnetic field is best described as a full spherical-harmonic
expansion [15]. Here, spherical-harmonic coefficients released as
the International Geomagnetic Reference Field (IGRF) are used
[16], in particular, the IGRF95 (or IGRF-7) model. All simulations in
this study use coefficients up to 10th degree and order. An important
note on the magnetic field is that it is represented in Earth-fixed
coordinates. The field itself is locked in step with the rotation of the
Earth [17]. One must be careful to distinguish between Earth-fixed
longitudes and inertial longitudes.
The effect of the Lorentz force on an orbit is conveniently broken up
into the components of the magnetic field in spherical-coordinate
unit vectors: the radial direction r^ , the colatitude direction φ^ , and
the azimuthal direction θ^ . The magnetic field B is studied as the
three components (B . r^), (B . φ^ ), and (B . θ^ ). Figure 2 shows a
contour plot of (B . r^) over (Earth-fixed) latitude and longitude at an
altitude of 600 km. Positive values are represented by dotted gray
contours, and negative contours are dashed gray. The black contour
is referred to as the magnetic equator and indicates where the radial
component is zero. In the traditional lexicon, the magnetic equator
is where the field has no inclination (or “dip”). For an axis-aligned
dipole model, the magnetic equator would lie on the latitudinal
equator, but the additional higher-degree terms modify its location
significantly.
80
60
40
20
0
-20
-40
-60
-80
-150 -100 -50 0 50 100 150
Figure 2. Contour plot of the radial component of the geomagnetic
field over latitude and longitude.
Figure 3 shows a contour plot of (B . φ ^ ). Again, dashed gray contours
are negative and dotted gray positive, with black being zero. The φ ^
component of the field is generally negative, except for small polar
regions. The φ ^ component is small near these polar regions and is
largest near the magnetic equator.
35

80
60
40
20
0
-20
-40
-60
-80
36
-150 -100 -50 0 50 100 150
Figure 3. Contour plot of the component of the geomagnetic field in
the φ ^ direction over latitude and longitude.
Figure 4 shows a contour plot of (B • θ ^ ). The contour colors are as
previously described. Figure 4 shows distinct regions of positive
and negative values. The zero contour represents the line of zero
declination (or zero difference between true north and magnetic
north). The dipole component of the field (and all other zero-order
terms) contributes nothing to the θ ^ component of the field.
80
60
40
20
0
-20
-40
-60
-80
-150 -100 -50 0 50 100 150
Figure 4. Contour plot of the component of the geomagnetic field in
the θ ^ direction over latitude and longitude.
The three orthogonal components of the field can be used to
divide the space of latitude and longitude into eight distinct zones.
The zones are defined by whether each component is positive or
negative and are bounded by the zero contours depicted in Figures
2-4. The zones are numbered I-VIII and are depicted graphically
in Figure 5 with the properties shown in Table 1. Figure 5 shows
each of the zones superimposed on a map of the Earth. Because
of distortion due to the map projection, zones I, II, VII, and VIII
are shown larger than their actual sizes. In a three-dimensional
General Bang-Bang Control Method for Lorentz Augmented Orbits
(3D) view, they appear in a small region near each pole. The large
southward swing of the zero declination contour over eastern Africa
actually crosses the magnetic equator, causing zones III and V to
have noncontiguous regions. Table 1 lists the differences among the
zones. A ‘+’ in the table refers to a quantity greater than zero and a ‘-’
denotes less than zero.
80
60
40
20
0
-20
-40
-60
-80
-150 -100 -50 0 50 100 150
Figure 5. Eight distinct zones of the geomagnetic field, numbered
I-VIII.
Table 1. Zone Properties.
Zone ( B • r ^ ) ( B • φ ^ ) ( B • θ ^ )
I + + +
II + + -
III + - +
IV + - -
V - - -
VI - - +
VII - + -
VIII - + +
In each zone, the geomagnetic field has a certain sign for a particular
component of the field. Each zone creates different effects on the
orbit of a charged satellite. We use these differences to create a
control sequence to perform a desired maneuver. The zones are
defined with respect to Earth-fixed latitude and longitude as the
geomagnetic field rotates with the Earth. Figures 2-4 are for a
representative altitude (600 km) because the relative strength
of each order of field terms depends on this altitude. Although
the actual zone boundaries depend on altitude, they are easily
calculated at any particular location by the simple sign definitions
shown in Table 1.

Space-Vehicle Design
This section offers a brief overview of possible architectures for
LAO-capable spacecraft. It considers three competing, interrelated
parameters: capacitance, power, and space-vehicle mass. There are
also implementation issues, such as deployability of the capacitor,
technology readiness of the power system, thermal implications of
high power, and interactions among various subsystems (notably
attitude control). These issues are minimized here. For the present,
maximizing the q/m metric is taken to be the only goal of LAO spacevehicle
design. Furthermore, we consider this metric only in terms
of a constant-mass spacecraft. Six hundred kilograms is chosen as a
somewhat arbitrary constraint for this mass optimization. The mass
is given some contingency.
Capacitance
High q/m implies high charge, which requires high capacitance.
Known technologies for self-capacitance store charge on the surface
of a conductor with no sharp local features or high curvature. So,
a successful design realizes high surface area to volume in flat
structures or long, thin ones. Such a capacitor likely encounters a
limit associated with the minimum thickness of thin films or the
minimum feasible diameter of long filaments. That limit ultimately
leads to a minimum mass for the capacitor. The capacitor is also
designed to exploit plasma interactions. Based on work by Choinière
and Gilchrist [18], we have baselined a cylindrical capacitor
constructed of a sparse wire mesh. This stockinglike arrangement
of appropriately spaced thin wires develops a plasma sheath due to
ionospheric interactions that raises the capacitance of the cylinder
well above what it would be in a pure vacuum. We emphasize that
such self-capacitance is not available from off-the-shelf electronics
components, which merely hold equal amounts of positive and
negative charge.
In this model, the capacitance C is taken to be that of a solid cylinder
of the stocking’s radius R, but with a concentric shell (due to the
plasma sheath) equal to the thickness of an individual wire’s sheath r : s
2pε L 0 C =
R + r
log s
(12)
R
where ε is the permittivity of free space. The sheath radius
0
increases with potential and is calculated as described by Choinière
and Gilchrist [18]. In the architecture described in the section
“Space-Vehicle Design: Power,” R takes on values of tens to hundreds
of meters. The sheath thickness r depends on the temperature
s
and density of the plasma and on capacitor potential, ranging
from millimeters to meters in earth orbit. We space these wires so
that one wire is a sheath’s thickness away from its neighbor. This
spacing ensures overlap between individual wires’ sheaths, but
keeps the structure sparse. Occasional structural elements, such
as thin conductive bands, would be necessary to maintain the
spacing along the capacitor because of coulomb repulsion that acts
among the wires. This repulsion would also serve as a useful means
for deploying the capacitor without heavy trusses or actuators. A
schematic of this design is shown in Figure 6.
General Bang-Bang Control Method for Lorentz Augmented Orbits
r s
Figure 6. LAO spacecraft with cylindrical stocking capacitor.
Power
We consider two fundamentally different approaches to the
power subsystem. The classical approach depends on solar power.
Energy from solar panels is used directly to power the capacitor,
countering the plasma currents, or is stored in batteries or some
sort of efficient ultracapacitor to be used in a periodic-charging
scheme. Some assumptions about the specific power (W/kg) must
be made. Although the power density of current systems is about 40
W/kg [19], a farther-term power density of 130 W/kg, is used here,
consistent with DARPA’s Fast Access Spacecraft Testbed (FAST)
program [20].
In the case of this solar-power approach, the charge is maintained
by modifying the current collection scheme proposed by Sanmartin
et al. [21]. A power supply onboard the spacecraft establishes a
potential between two conductive surfaces exposed to the plasma
environment. The positive end attracts the highly mobile electrons,
while the negative end attracts the far less mobile ions (such as
O+). The substantial imbalance in electron and ion currents leads
the negative end to accumulate a nonzero charge while the positive
end is almost electrically grounded in the plasma. So, with the
wire capacitor on the negative end, the spacecraft would achieve
a net charge roughly equal to the product of the capacitance of the
wires and the potential across the power supply [18]. This charge is
accomplished without the use of particle beams.
A more unusual approach exploits alpha-particle emission from
an appropriate radioactive isotope [22], such as Po 210. These
emissions are not converted to electrical power thermionically
as in a radioisotope thermoelectric generator or via fission in a
nuclear reactor; instead, the isotope is spread thinly enough on the
capacitor’s surface that up to half of the emitted alpha particles
carry charge away from the spacecraft. The electrical current
of these particles is proportional to their charge (two positive
L
R
37

fundamental charges), their kinetic energy (roughly 5.3 × 106 eV), and the isotope’s decay rate. If the maximum potential can
be achieved despite currents from the surrounding ionospheric
plasma, this approach offers as much as 42 kW/kg of Po 210 after
1 year of alpha decay. Maintaining this charge requires no power
supply. The spatially distributed nature of the current from the thin
film suggests that the current does not approach any sort of beamdensity
limit due to space charge.
We focus on the prospects for the solar-panel approach because
launching an isotope is likely to encounter a variety of technical
and nontechnical roadblocks. In all cases, the capacitor maintains
negative charge. The ion currents are then given by the orbit
motion limited estimate [18]. We use the International Reference
Ionosphere (IRI) [23] to provide the necessary plasma number
density and temperature. We also account for the photoelectric
current emitted from the surface of the conductive capacitor. In
the case of the solar panel approach, all this power is subject to
resistive losses as the power supply drives current through the many
thin wires. Assuming that the current is uniform to all parts of the
capacitor, we average the losses along the length of wire that the
current has to travel.
Space-Vehicle Mass
The charge-to-mass ratio depends on the mass of the entire
space vehicle. We model this mass coarsely as the sum of discrete
components with interrelated dependencies. Table 2 summarizes
this mass model.
An example of the power calculation is shown in Table 3. Table 4
uses this power calculation to arrive at the 600-kg space-vehicle
mass requirement.
Performance Estimates
Figure 7 summarizes the results of these calculations for a 600-kg
spacecraft that charges for 50% of the time over a 600-km orbit.
Figure 7 shows the FAST power design, which yields q/m = 0.0070
C/kg for a 20-km stocking at a 7- kV potential. The efficiency (force
per power) increases with lower potential. For example, the optimal
value of 5C in a 600-km polar orbit produces about 2.3 N, for 1.6
× 10-5 N/H when the capacitor is charged. However, at only 1 kV,
the resulting 3.1C represents 2 × 10-5 N/W. So, if the speed of the
maneuver is unimportant, lower-potential designs may be better.
As the capacitor potential increases beyond the optimum for
q/m, more mass of the fixed 600 kg must be devoted to the power
subsystem, which comes at the expense of capacitor mass. The
accuracy of these performance measures depends on the accuracy
of the simplified sheath model and should eventually be verified by
a more complex 2D algorithm such as that developed by Choinière
and Gilchrist [18].
38
Table 2. Space-Vehicle Mass Model.
General Bang-Bang Control Method for Lorentz Augmented Orbits
Subsystem or Component Value Units
Payload 50 kg
Bus (w/payload power) 3.33 (kg bus)(kg payload)
LAO solar power 130 W/kg of orbit-average
power
LAO isotope power 42 kW/kg of polonium
after 1 yr of decay
Power mass contingency 14 kg
Capacitor 2700
pR 2 nL
Capacitor mass
contingency
kg for n aluminum
wires of length L and
radius R
1.1 m kg, where m is the
sum of the wires’
masses
Table 3. Example of Power Calculation for a Spacecraft in a 600km
Altitude LEO Circular Orbit at an Inclination of 28.5 deg.
Parameter Value Units
Wire material Aluminum
Wire radius 500 × 10 -6 m
% overlap sheath diameter 0%
Length of stocking, L 20 km
Stocking radius as a % of stocking length 5.00%
Stocking mass sandbag 3 kg
Immediate calculation Value Units
Material resistivity at 20°C 2.82 x 10-8 Ω
Radius of stocking, R 1 km
Material density of wire 2700 kg/m 3
Sheath thickness 1.764 m
Resistance per wire 7.198 M Ω
Number of wires 7142
Mass of stocking 30.36 kg
Mass of capacitor 33.40 kg
Average cylinder-as-body capacitance, C 600 × 10 -4 F
Result Value Units
Average body charge-to-mass ratio -0.0070 C/kg
Exposed wire area 2548 m 2
Photoelectron current 0.122 A
Orbit-average power required (~50%
duty cycle)
53.54 kW

Table 4. Example of Mass Calculation.
Parameter Value Units
Potential -7000 V
Orbit-average power for
LAO
LAO power system mass
dependency
46.28 kW
130 W/kg
LAO power system mass 466 kg
Power system mass
contingency
14 kg
Payload 20 kg
Bus mass (including any
propellant)
100 kg
Total space-vehicle mass 600 kg
Approximate Orbit-Average Power (W)
Approximate Orbit-Average q/m, (C/kg)
70000
60000
50000
40000
30000
20000
10000
0
0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000
Capacitor Potential (V)
0.008
0.007
0.006
0.005
0.004
0.003
0.002
0.001
0
0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000
Capacitor Potential (V)
Figure 7. Orbit-average power and q/m vs. capacitor potential.
Lorentz Augmented Orbit Maneuvers and Limitations
Maneuver Limitations
A Lorentz augmented orbit cannot experience arbitrary changes for
all initial orbital elements. In certain regimes, as evidenced by Eq.
(10), changes in orbital elements are tightly coupled. This coupling
stems from the basic physics of the Lorentz force. The direction
General Bang-Bang Control Method for Lorentz Augmented Orbits
of the force is set by the magnetic field and the velocity of the
spacecraft with respect to that magnetic field, neither of which can
be altered by the spacecraft control system.
A further limiting factor is that the best system architectures
provide only one polarity of charge (negative). Because electrons
in the ionosphere are far more mobile than ions, significantly less
power is required to maintain a negative charge than a positive
charge. The single-polarity system limits what changes can be made
to the RAAN Ω and the argument of perigee w. For a given charge
polarity, Ω and w evolve only in a single direction (in LEO). For a
negative charge in LEO, Ω always decreases and w always increases.
Table 5 summarizes some of the abilities and limits of LAO for a
single polarity of charge. The first column of the table shows the
net effect of a constant charge on a spacecraft. The second column
shows the available directions of change for each orbital element for
a variable (but single polarity) charge. The final column summarizes
some the special cases and coupling within the dynamics. Some of
these special cases are addressed more explicitly in our earlier work
[2], [3].
Table 5. LAO Effects for q/m < 0 in LEO.
Element Net Effect
of Constant
Charge
Signs of
Possible
Changes
Notes
a 0 ± a/i coupled for e = 0 or i =
90 deg,
e 0 ± a • = 0 for i = 0 deg and e
= 0
i 0 ± ė > 0 for e = 0
Ω - - a/i coupled for e = 0 or i
= 90 deg
w + + Ω undefined for i = 0 deg
ν ± w undefined for i = 0 deg
and e = 0
The Lorentz force is at its strongest in LEO. The strength of the
dipole component of the magnetic field drops off with the cube
of radial distance. Additionally, spacecraft velocities with respect
to the magnetic field tend to be larger in LEO. A geostationary
spacecraft has no velocity with respect to the magnetic field and
thus experiences no Lorentz force.
Example Maneuver: Low-Earth-Orbit Inclination Change and
Orbit Raising
The minimum inclination a spacecraft can be launched into is equal
to the latitude of its launch site. For a U.S. launch, this minimum
inclination is generally 28.5 deg, the latitude of Cape Canaveral,
FL. However, for certain missions, equatorial orbits are desirable.
The plane change between i = 28.5 deg and i = 0 deg is expensive
in terms of ΔV and requires either a launch vehicle upper stage or
a significant expenditure of spacecraft resources. We develop a
39

control algorithm to use the Lorentz force to perform this inclination
change without the use of propellant, while simultaneously raising
the orbital altitude.
This maneuver is primarily concerned with inclination change in
circular orbit. Equation (10) describes the relevant dynamics. As
energy change and inclination change are coupled in this situation,
Eq. (8) describes both the energy and plane changes. In this circular
case, only the radial component of the magnetic field affects the
energy and inclination. For the inclination to decrease, the energy
must increase. With these facts, we develop a bang-off controller
based on the argument of latitude and the sign of the radial
component of the field. Using q/m < 0, the term cos u(B • r^) must
be negative. We know that (B • r^) is positive below the magnetic
equator (zones I, II, III, and IV) and negative above the magnetic
equator (zones V, VI, VII, and VIII). Thus, for northward motion of
the satellite (cos u > 0), the charge should be nonzero within zones
V-VIII. For southward satellite motion (cos u < 0), nonzero charge
is applied in zones I-IV. In other words, the charge should be off for
the first quadrant of the orbit, on for the second quadrant, off for
the third, and on for the fourth. This control can be represented as
40
q
m =
q
–( m)
max
0
q
–( m)
max
0
if cos u > 0, (B . r ^ ) < 0
if cos u > 0, (B . r ^ ) > 0
if cos u < 0, (B . r ^ ) > 0
if cos u < 0, (B . r ^ ) < 0
(13)
where -(q/m) is largest available negative charge-to-mass ratio.
max
However, when this simple quadrant control is used, the eccentricity
of the orbit tends to grow undesirably large. Maintaining an identically
zero eccentricity is impossible, though. Any charge on a circularorbiting
spacecraft causes an increase in the eccentricity. However,
if the oblateness of the Earth is considered, the eccentricity remains
bounded by a small value. Figure 8 shows this result, plotting a short
simulation of an orbit under the quadrant controller. The blue line
shows the growth of eccentricity with J2 absent, while the green
line shows the bounding of e under the influence of J2. The effect of
J2 on the eccentricity of the orbit is larger than that of the Lorentz
force. The J2 perturbation does not affect the overall performance
of the maneuver, though. The Lorentz force depends on the velocity
of the spacecraft, which only changes by small amount due to the
presence of J2. The presence of J2 only creates a small periodic
disturbance to both a and e.
Figure 9 shows the results of a simulation using the e-limiting
quadrant method. The simulation begins with a 600-km altitude
circular orbit. The charge-to-mass ratio is q/m = -0.007 C/kg. A full
model of J2 is used. The simulation lasts until an equatorial orbit is
reached. The IGRF95 magnetic field model is used to 10th degree
and order. Figure 9a shows the increase in semimajor axis given
by the quadrant method. The initial 600-km orbit is raised to a
724.0-km circular orbit, an increase of 124 km. Figure 9b shows the
desired decrease in inclination. Because the magnetic equator does
not align with the true equator, the inclination can be brought to
exactly zero. Zero inclination is reached in about 340 days with this
value of charge. Figure 9c shows the eccentricity. The eccentricity
is bounded by the J2 perturbation to a small value. Finally, Figure
General Bang-Bang Control Method for Lorentz Augmented Orbits
km
0.03
0.025
0.02
0.015
0.01
0.005
7200
7100
7000
6900
0 200 400
0
0 200 400
Time (days) Time (days)
a) Semimajor Axis, a
b) Inclination, i
0.015
0.01
0.005
J2 Present
J2 Absent
0
0 10 20 30
Time (days)
40 50 60
Figure 8. Effect of Earth oblateness on the eccentricity under the
quadrant control.
0
0 200 400
Time (days)
Eccentricity, e
Angle (degrees)
Angle (degrees)
30
20
10
200
-200
0 200 400
Time (days)
c) Eccentricity, e d) Right Ascension, Ω
Figure 9. Orbital elements for the LEO plane change and
orbit-raising maneuver.
9d shows the RAAN. For a negative q/m in LEO, the RAAN always
decreases; however, in this simulation, the effect of J2 on RAAN
dominates. If the aforementioned simulated maneuver is performed
using conventional impulsive thrust, it requires a ΔV of 3.75 km/s.
Thus, using LAOs could significantly increase the payload ratio of a
spacecraft that needed such a maneuver. However, this mass savings
comes at a cost of time spent, the mass of the capacitor and power
system, and electrical power consumed during the maneuver.
0

Lorentz Augmented Orbit Power Consumption and
Plasma-Density-Based Control
The preceding simulations use a code that does not include a
model of the Earth’s ionosphere. The spacecraft design process
is carried out initially using the IRI model, and then that design
is used in simulation. The simulation assumes the spacecraft
maintains its design charge-to-mass ratio, regardless of the local
plasma conditions. In this section, we explore the use of a more
in-depth LAO simulation by revisiting the LEO inclination-change
maneuver. This simulation uses a code that takes into account
local ionospheric conditions and their effect on the instantaneous
charge-to-mass ratio and power consumption of the spacecraft.
The high-fidelity, plasma dynamics simulation is based on the
Global Core Plasma Model (GCPM) [24]. The GCPM model is
a framework for blending multiple empirical plasma-density
models and extending the IRI model to full global coverage. For
the next simulations, the GCPM model at one particular time is
used. This time corresponds to mean solar conditions. Although
there is a strong correlation between plasma conditions and time
of day, this effect is averaged out by simulating over the course of
multiple days.
This simulation functions in a different fashion from the results
presented in the “Lorentz Augmented Orbit Maneuvers and
Limitations” section. The earlier simulations assume that q/m
is either zero or constant at a value of -0.007 C/kg. The GCPM
simulation assumes that the spacecraft maintains a constant
potential on the capacitor. Because of local variations in plasma
density, a constant potential results in varying values of chargeto-mass
ratio and varying power required to hold the constant
potential. Although the mean q/m and orbit-average power are
consistent with those predicted in our earlier analysis in the
“Space-Vehicle Design” section, they have peak and minimum
values that depend on the local plasma environment.
The local electron number density n is a strong predictor of power
e
usage and is readily available from the GCPM model. A higher ne General Bang-Bang Control Method for Lorentz Augmented Orbits
corresponds to a denser plasma, which in turn, results in more
current collection for a stocking at a given potential. Thus, high-ne values correlate to high power usage. In a gross sense, n is larger
e
in the low- to mid-latitudes on the daytime side of the Earth. The
density of the plasma also drops sharply as a function of altitude.
Assuming the spacecraft has knowledge of its local plasma
conditions, significant power savings can be realized by limiting
the charge-on time when n is high. The spacecraft simply follows
e
its normal control law, but turns off the charge whenever ne exceeds a particular value. A sample of this power savings and
cost in time is shown in Table 6. This table lists the results of four
simulations performed with the constant-potential code. Each
simulation integrates over three days and begins with the same
initial conditions. Reported for each run is the mean q/m achieved
(during times of nonzero charging), the average power used (over
the entire simulation), the peak instantaneous power used, the
total inclination change over the simulation, and an efficiency
in the form of degrees of inclination change per day divided by
the average power used. The first simulation uses the e-limiting
quadrant controller discussed earlier with no modification based
on electron density. The other three runs superimpose an n - e
based, density-limited control, turning off the charge when n is e
greater than some value.
The average q/m achieved by each successive simulation is
slightly lower, as seen in the first row of Table 6. In regions of high
plasma density, the capacitance of the stocking is increased by a
tighter plasma sheath, leading to a larger capacitance. However,
this increase in q/m requires significantly more power to maintain,
as the denser plasma greatly increases the current collected by the
stocking. The power reduction due to density-limited control is
shown in the second and third rows of Table 6. Without densitybased
control, the average power usage over the simulation is
53.54 kW, but with a peak instantaneous power usage of 418.57
kW. When charge is only applied for an n of less than 1.1 × 10 e 11
m-3 (the mean electron density in this orbit), the power usage
drops to a mean of 12.94 kW, with a peak of 89.59 kW. Of course,
Table 6. Limiting Power Usage via n e (in m -3 ) Sensing for 3-day Simulations at an Initial Inclination of i = 28.5 deg at a 600-km Altitude.
No n e Control ne < 2 × 10 11 n e < 1.5 × 10 11 n e < 1.1 × 10 11
(q/m) mean , C/kg -0.0060 -0.0057 -0.0054 -0.0048
P mean , kW 53.54 35.88 24.33 12.94
P peak , kW 418.57 220.01 140.64 89.59
Δi, deg 0.3764 0.3292 0.2653 0.1747
deg/day/kW mean 0.0023 0.0031 0.0036 0.0045
41

the decreased power usage is coupled with a lengthening of the
maneuver time. Row 4 of Table 6 shows the inclination change
achieved over 3 days for each level of density control. The
unlimited control changes inclination at a rate about 2.2 times
higher than the n < 1.1 × 10 e 11 case. However, the density-limited
controllers achieve inclination changes in a more efficient way.
The fifth row of Table 6 displays an efficiency metric for each
simulation, namely degrees of inclination change achieved per day
per average kilowatt used. Charging only at low values of n uses e
the available power more efficiently to effect inclination change.
The profile of electron densities experienced by a spacecraft varies
greatly depending on its orbit. In the 28.5-deg inclination-change
example, both the change in inclination and the change in altitude
during the maneuver cause no one limit on n to be appropriate.
e
However, recreating this entire maneuver using the GCPM, constant
voltage simulation is impractical in its computational demands. A
reasonable approximation is a hybrid simulation in which a constant
charge-to-mass ratio is used, but the electron density is calculated
at each step in the integration to superimpose the density-limited
control strategy. To take advantage of the orbit raising that occurs
during the maneuver, the n cutoff value is made a linear function
e
of the spacecraft altitude. This line is defined by two points: n equal
e
to 2.0 × 1011 m-3 at an altitude of 600 km, and n equal to 1.6 ×
e
1011 m-3 at an altitude of 700 km. These values are chosen to give
a reasonable tradeoff between power savings and maneuver time.
Figure 10 shows the results of this hybrid simulation. The top plot
of this figure shows the semimajor axis, while the lower plot gives
the inclination, both versus time in days. The solid green lines are
the results of the hybrid constant q/m, density-limited simulation.
For comparison, the dashed blue lines show the results of the
constant charge-only simulation. The hybrid strategy completes the
inclination-change maneuver in 380 days compared with 340 days
for the original strategy.
To provide insight into the power saved by using the density-limited
hybrid strategy, short-duration simulations are run using the full
GCPM, constant voltage code. These simulations are run for three
42
General Bang-Bang Control Method for Lorentz Augmented Orbits
points in the trajectory of both the hybrid simulation and the original
inclination-change maneuver. When each trajectory reaches 28.5,
10, and 1 deg of orbital inclination, its state is retrieved and used
as the initial conditions for a 3-day simulation using the full GCPM
code. The results of these simulations are summarized in Table 7.
This table gives the mean achieved charge-to-mass ratio, average
and peak power consumptions, and inclination change over the
3-day simulation for each control strategy for each inclination
considered. The addition of density-limited control reduces both
the mean and peak power usage, but also decreases the speed of the
inclination change.
km
Angle (degrees)
7200
7100
7000
6900
0 100 200
Time (days)
n Control
e
No n control
e
300 400
a) Semimajor axis, a
30
20
10
b) Inclination, i
Time (days)
n e Control
No n e control
0
0 100 200 300 400
Figure 10. Comparison of hybrid simulation of constant charge-tomass
ratio with plasma-density-limited control to constant q/monly
control.
Table 7. Comparison of Power Usage During both the Hybrid Simulation and the Original, Constant Charge Simulation.
Inclination Constant Charge Hybrid, Density-Limited
28.5 deg (q/m) mean , C/kg
P mean (P peak ), kW
Δi, deg
10 deg (q/m) mean , C/kg
P mean (P peak ), kW
Δi, deg
1 deg (q/m) mean , C/kg
Pmean(P peak ), kW
Δi, deg
-0.0060
53.54 (418.57)
0.3764
-0.0055
56.79 (259.47)
0.1806
-0.0055
58.39 (235.89)
0.1447
-0.0057
36.50 (217.15)
0.3308
-0.0053
43.64 (208.58)
0.1622
-0.0053
43.54 (208.39)
0.1330

Conclusions
Lorentz augmented orbits use the Earth’s magnetic field to provide
propellantless propulsion. Although the direction of the Lorentz
force is fixed by the velocity of the spacecraft and the local field,
varying the magnitude of the charge-to-mass ratio of the satellite
can produce novel and useful changes to an orbit. A simple onoff
(or bang-off) charging scheme is sufficient to perform most
available maneuvers and can create large ΔV savings.
A preliminary evaluation of some possible architectures leads us
to the tentative conclusion that up to 0.0070 C/kg can be reached
by a negatively charged LEO spacecraft of 600-kg mass. These
designs use cylindrical mesh “stocking” capacitive structures that
are shorter than most proposed electrodynamic tethers and offer
the important benefit that their performance is independent
of their attitude in the magnetic field. That simplicity largely
decouples attitude control from propulsion, a consideration that
can complicate the operation of tether-driven spacecraft.
The Earth’s magnetic field is a complex structure. Accurate analytical
expressions for orbital perturbations are difficult to obtain. The
proposed control method accommodates this complexity by
breaking the geomagnetic field into distinct zones based on its
sign in three orthogonal directions, leading to eight zones. Within
each zone, an LAO tends to evolve in certain directions for certain
orbital elements. Understanding how the orbital evolution relates to
the zone the spacecraft is in allows us to develop control strategies
to execute complex maneuvers. A simple, but effective strategy is
to operate a bang-off control scheme that switches only at zone
boundaries. This scheme allows for the execution of a sample
maneuver of a LEO plane change without the use of propellant,
saving a ΔV of 3.75 km/s required for a conventional propulsive
maneuver. However, this maneuver lasts for 340 days and requires
about 53 kW of power on average. A controller that limits charging in
response to local plasma-density measurements reduces this power
requirement to an average of 40 kW, but increases the maneuver
time to 380 days.
General Bang-Bang Control Method for Lorentz Augmented Orbits
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44
General Bang-Bang Control Method for Lorentz Augmented Orbits

Brett J. Streetman is currently a Senior Member of the Technical Staff at Draper Laboratory working primarily
in space systems guidance, navigation, and control (GN&C). At Draper, he has worked on the Talaris Hopper, a
joint MIT and Draper Lunar and planetary hopping rover GN&C testbed, performed control system analysis for
the International Space Station, and worked on the GN&C system for the guided airdrop platform. Dr. Streetman
received a B.S. in Aerospace Engineering from Virginia Tech and M.S. and Ph.D. degrees in Aerospace Engineering
from Cornell University.
Mason A. Peck is an Associate Professor in Mechanical and Aerospace Engineering at Cornell University. His
research focuses on spaceflight dynamics, specifically, the discovery of new behaviors that can be exploited for
mission robustness, advanced propulsion, and low-risk GN&C design. He holds 17 U.S. and European patents in
space technology. Dr. Peck earned B.S. and B.A. degrees from the University of Texas at Austin, an M.A. from the
University of Chicago, and Ph.D. and M.S. degrees from UCLA.
General Bang-Bang Control Method for Lorentz Augmented Orbits
45

46
The U.S. military’s unmanned aircraft systems are constantly
gathering an enormous amount of video imagery, but much of it is
not useful to tactical forces due to a shortage of analysts who are
needed to process the information.
This paper examines four automated methods that address
the military’s requirements for turning full motion video into a
functional tool for a wide variety of tactical users.
The authors have demonstrated the feasibility of these methods,
and could complete the development and testing needed for
operational use within 3 years if funding is made available.
Tactical Geospatial Intelligence from Full Motion Video

Tactical Geospatial Intelligence from Full
Motion Video
Richard W. Madison and Yuetian Xu
Copyright © by IEEE. Presented at Applied Imagery Pattern Recognition 2010: From Sensors to Sense (AIPR 2010), Washington D.C.,
October 13–15, 2010
Abstract
The current proliferation of Unmanned Aircraft Systems provides an increasing amount of full-motion video (FMV) that, among other
things, encodes geospatial intelligence. But the FMV is rarely converted into useful products, thus the intel potential is wasted. We have
developed four concept demonstrations of methods to convert FMV into more immediately useful products, including more accurate
coordinates for objects of interest; timely, georegistered, orthorectified imagery; conversion of mouse clicks to object coordinates;
and first-person-perspective visualization of graphical control measures. We believe these concepts can convey valuable geospatial
intelligence to the tactical user.
Introduction
Geospatial intelligence, which includes maps, coordinates, and
other information derived from imagery [1], can address many of
the intelligence needs of a tactical user [2], [3]. A potentially rich
source of imagery to inform this geospatial intelligence is the Full
Motion Video (FMV) from the U.S. military’s thousands [4], [5] of
fielded Unmanned Air Systems (UASs). Current programs promise
to dramatically increase the number of FMV feeds in the near future
[6], [7]. However, there are too few analysts to process that flood
of FMV [8], thus much of it goes unused. At the tactical echelons,
raw FMV simply is not used to generate geospatial intelligence [9].
We have developed four concept demonstrations to show how
FMV could be shaped into potentially useful forms of geospatial
intelligence. This paper describes the four demonstrations in more
detail.
In the first demonstration (“Object-of-Interest Geolocation” section),
we used the contents of Predator FMV to improve the accuracy of
telemetered locations of objects by an order of magnitude, averaged
over 4000 image frames. Our contributions include one-time userassisted
frame alignment, telemetry extraction, altitude telemetry
correction, target tracking, and roll estimation.
In the second demonstration (“Orthorectified Imagery” section),
we combined image stitching with extracted telemetry to generate
orthorectified and georegistered imagery of an area overflown by a
Predator. This imagery could be produced in short order by brigadelevel
air forces to allow ground assets to navigate an area that has no
existing up-to-date maps or imagery.
In the third demonstration (“Metric Video” section), we used
transforms between FMV frames and orthorectified imagery to
recover the ground coordinates of objects clicked on in the FMV.
This involved automatically detecting, monitoring, and updating
the coordinates of moving objects.
Tactical Geospatial Intelligence from Full Motion Video
In the fourth demonstration (“Video Markup” section), we used the
same transforms to project graphical control measures drawn over
the orthorectified imagery back into the FMV, allowing a user to
see how objects in the video move relative to the control measures,
facilitating rehearsal and/or after-action review.
Object-of-Interest Geolocation
One particularly useful form of geospatial intelligence is the
coordinate of an object seen in FMV. This could be used, for instance,
to call in fire, dispatch forces, cue a sensor, or retrieve locationrelevant
video from an archive. Previous object geolocation work
at Draper Laboratory [10] focused on sensor-disadvantaged,
small UASs triangulating object coordinates from multiple looks.
Larger vehicles, such as Predators, could combine accurate Global
Positioning System (GPS)-Inertial Navigation System (INS), laser
ranging, onboard Digital Terrain Elevation Data (DTED), etc., to
identify target coordinates from a single look. Coordinates of the
ground “target” at the center of the Predator’s camera reticule can
be calculated and overlaid in real time on the camera feed. However,
are those coordinates sufficiently accurate that one could call in fire
on the correct target or extract archive video of just the target and
not the whole neighborhood? We assert that the content of the FMV
could be used to improve the accuracy.
In the first concept demonstration, we showed how the accuracy of
Predator target telemetry can be improved by an order of magnitude
with a little operator intervention and image processing. Table 1
shows the relative magnitude of error in object geolocation that we
observed with raw and improved Predator target telemetry.
We began our pursuit of better geolocation with a simple experiment
to evaluate the accuracy of Predator’s target telemetry. We obtained
unclassified video from a Predator camera following a truck as it
winds through a small town. A sample frame is shown in Figure 1.
The targeting telemetry is easily good enough to call up a Google
47

Table 1. Relative Error in Target Lat, Lon at Stages of Improvement
Condition Observed Error Improvement
Target lat, lon from CC
stream
Target lat, lon from
image overlay
Plus GUI-based
altitude correction
48
100% 1×
115% 0.87×
22% 4.6×
Plus image processing 7% 13.4×
Figure 1. Example of a frame of the video sequence as a truck drives
through the town.
Earth [11] map of the town. Watching the video, we observed the
path taken by the truck and measured the coordinates of that path
on the Google Earth map. This formed the “ground truth.” At each
of approximately 4000 frames of video, we compared the ground
location of the truck (per the telemetry overlaid on the video)
against the nearest point on the truck’s ground truth trajectory.
We declare the mean distance to be the Predator’s target telemetry
error. Similarly, we compared the ground locations reported in
the video’s closed caption telemetry stream against the ground
truth over the same interval. The closed caption stream provided
our “baseline” error. The mean error based on the video overlay
was approximately 115% of baseline error, reflecting some errors
in the automatic optical character recognition we used to extract
telemetry from the screen.
Figure 2 shows the telemetered (and processed) paths of the
observed truck overlaid on the Google Earth map. At first glance, the
locations given by the raw telemetry may seem shifted left relative
to the map. However, the shift actually varies with camera azimuth.
The error is best explained by an offset in the camera’s estimated
height above target such as might arise from inaccuracy of the
GPS altitude [12], [13], barometric altimeter, or DTED. To find the
ground location of a target, as shown in Figure 3, one begins at the
Tactical Geospatial Intelligence from Full Motion Video
camera location and extends the camera’s line-of-sight (LOS) until
its height matches the camera’s estimated height above target. If the
vehicle operates at any reasonable standoff, the LOS has only slight
depression, and a few meters of error in the UAS’ altitude estimate
corresponds to many meters of lateral error in the target coordinate.
The telemetry contains an accurate camera azimuth, so if we know
the altitude error and if the terrain is flat near the target, we can
calculate the lateral error in the target coordinate.
Figure 2. Path followed by a truck observed in Predator video,
according to, in order of increasing accuracy, telemetry overlay
(yellow), closed-caption telemetry (brown), altitude-corrected
telemetry (pink), and fully corrected telemetry (orange).
Figure 3. Lateral distance from aircraft to target is a function of
camera depression angle and altitude above target. For slight
depression angles, a small inaccuracy in altitude produces a much
larger error in lateral distance.
Conversely, we can calculate altitude error given lateral offset of the
target. We implemented a Matlab script that projects a single frame
of Predator video onto the ground plane based on the telemetry
overlaid on that image. It saves the projection as an image and a
KML file. A user imports the projection into Google Earth, shifts it
to align visually with the map, and saves it. In theory, this could be
done automatically, but the difference in camera modalities (EO vs.
infrared (IR)), perspectives (low oblique vs. nadir pointing), and
capture times (potentially many seasonal and illumination changes)

make the task difficult to automate, yet comparatively easy for a
human. A second script uses the observed shift and the Predator
camera pointing angles (given in the telemetry) to calculate the
corresponding altitude offset. This offset is subsequently applied
to all telemetry to calculate revised target coordinates, improving
mean accuracy by about a factor of 4.6. The resulting path is shown
in pink in Figure 2.
We can do even better with some image processing and filtering.
First, we track the 2D location of the target in the video. Predator
target telemetry is based on camera pointing, so when the operator
does not hold the camera on target, the telemetry is inaccurate.
From information such as the 2D location of the target in each
image, the camera pointing angles, field of view, and the corrected
altitude yielded by the process described in the paragraph above,
we can calculate the ground location of the target wherever it moves
in the image. Figure 2 shows the impact where the orange and pink
lines diverge at the center and bottom left of the figure.
The calculation requires an estimate of camera roll. This does not
explicitly appear in either the telemetry overlay or the closedcaption
(CC) telemetry stream. However, it can be inferred from the
vehicle orientation and camera azimuth and elevation recorded in
the CC telemetry stream. Those values are not synchronized with
each other or the video, but we can roughly synchronize the CC and
video streams by finding the time offset that best aligns the contents
of the target location and camera location fields, which appear in
both streams.
Next, we filter the telemetry extracted by optical character
recognition to eliminate sharp jumps in target location. Such a jump
occurs in the center of Figure 2, where the trajectory jumps to the
left briefly as the truck turns a corner. Here, the ground falls away
into a ravine to the left, the LOS from the camera far to the right
roughly parallels the ground slope and thus intersects far to the left.
Our altitude correction cannot overcome this much error. However,
filtering drastically reduces this overshoot. After image processing
and filtering, the trajectory shown in Figure 2 by the orange line
represents a 13.4× improvement in mean error in the location of
the tracked truck compared with the raw Predator target telemetry.
This demonstration shows good accuracy improvement for a single
image sequence. It is limited by its assumption of locally level terrain,
but this limitation could be removed by incorporating a DTED into
the correction process. Observed performance improvement will
vary to the extent that the Google Earth map coordinates deviate
from truth and as the altitude error varies over time and across
videos.
Orthorectified Imagery
Another potentially useful form of geospatial intelligence is timely,
orthorectified imagery. A tactical end user, for instance at the
company or platoon level embarking on a mission may appreciate
intelligence about his area of operations, such as the fact that a
particular bridge is out, a river is full, trees block intervisibility in
certain key places at this time of year, and he will be observed by the
shepherds whose herd of goats is grazing along his intended route.
A recent survey shows that he will often be given maps 15 years
out of date [9]. Nor does he likely have real-time access to satellites
Tactical Geospatial Intelligence from Full Motion Video
or photogrammetrists to generate up-to-date, georegistered,
orthorectified imagery of his small area of operations. However, he
may warrant time from brigade-level air assets. Perhaps a Predator
can provide FMV, which can be used to generate orthorectified,
map-registered, up-to-date imagery to support his mission.
To test this theory, we orthorectified imagery from the Predator
video used in the previous demonstration. We used the same
graphical user interface (GUI) to correct the telemetered altitude,
latitude, and longitude of ground at the center of the image. These
and the telemetered camera pointing angles define a transform
between image coordinates and ground coordinates, assuming flat
ground. We selected key frames (every 50th frame) from the video
and extracted interest points based on SIFT descriptors [14]. We
used the algorithm of [15] to automatically detect reliable sets of tie
points matched across images. We retained tie points that appeared
in at least four images and projected them to the ground using their
images’ image-to-ground transforms. The transforms are predicated
on flat terrain and perfect telemetry, neither of which were available,
so the projections of matching tie points are imperfect and form
clusters in the neighborhood of their correct location.
Next, we used an Expectation Maximization (EM) approach to
determine a single coordinate for each cluster of matched tie points.
The approach consists of a loop wherein each iteration (1) finds
the center (mean location) of each cluster and (2) modifies each
image’s image-to-ground transform to better align the tie points to
the corresponding cluster centers. This loop continues until the
calculated changes in cluster centers and transforms are all small.
In both phases, weighting favors tie points that appear in more
frames (better for enforcing consistency), are better aligned (less
likely to be outliers), or come from images with few tie points (to
avoid ignoring these images). The weights gradually evolve to favor
tie points in images with better telemetry. The approach is inspired
by Google PageRank [16], which solves the analogous problem of
evolving weights to favor more authoritative web pages connected
by hyperlinks.
The EM loop runs twice, the first time adjusting image-to-ground
transforms by only translation along the ground and the second
time finding a full homography for each image to best align tie
points. The second pass solves for more parameters and would be
poorly conditioned if not for the elimination of bulk translation
from the first pass.
The EM approach was chosen because it aligns images yet respects
the global shape of the image set provided by the telemetry. This
compares favorably with three other potential approaches. Simply
projecting images based on their telemetry would provide an
image set with good global accuracy, but the individual images
would not align, so it would be unclear how to combine them into
a single mosaic. Conversely, a pure image-stitching algorithm would
align the image content, including error in the image content. This
error would compound as a map grew around the single frame
that was manually aligned in the GUI, such that the geometry of
image content would rapidly deviate from reality with distance
from that single image. An obvious compromise is a weighted least
squares solution that attempts to find the set of image transforms
49

that minimizes both the size of tie point clusters and the distance
from the telemetered transform. But it is unclear how to define a
meaningful distance in image transform space or how to weight
error there relative to pixel distance. The chosen algorithm avoids
these problems by minimizing error purely in image space.
After the EM process converges to a set of consistent image-toground
transforms, we use the transforms to combine the images. We
normalize the intensity of the images so that frames throughout the
sequence have comparable mean intensity. We project each image
into ground coordinates and resample into a common grid of pixels.
For each pixel in the grid, we identify the set of projected images
that overlap at that pixel, extract the intensities at that pixel, and
record the median intensity. Thanks to the earlier intensity scaling,
the set of intensities at a pixel tends to be a tight cluster except for
outliers caused by transient objects moving through the pixel over
the course of a few frames. Thus, median filtering excludes most
moving objects from the mosaic. Slowly or infrequently moving
objects, e.g., cows, may remain in the mosaic, especially in areas
built from a small number of images.
Figure 4 shows the resulting imagery overlaid on an actual map.
It has several valuable traits. First, it shows physical objects
that ground troops can compare visually to objects seen in the
environment. A building on the image looks like a building that is
truly in the scene. This compares favorably to a contour map, which
probably does not depict the building, or a year-old image where
the building may have a different shape. Second, because the data
come from oblique-view video, the buildings are rendered from
oblique view, which may be easier for a human to parse than an
overhead view. Third, objects seen in video can be located precisely
on this imagery. This may be more intuitive than GPS coordinates
as a way to convey a precise location to a soldier. Fourth, the new
imagery overlays existing imagery and is georegistered moderately
well based on corrected telemetry. The existing imagery provides
context and archival data where current data are not available.
The image stitching implementation we used presumes locally
flat terrain and requires overlap between images. Our imagery
violated both conditions, so the orthorectified image is distorted
in several places. We have begun work to reduce or eliminate these
dependencies to produce less distorted imagery. However, this
may be unnecessary, because even if orthorectification is distorted
and/or georegistration is inaccurate, and even if GPS is denied,
a coordinate-seeking infantry platoon can navigate directly to a
target marked on the up-to-date and easy-to-understand imagery.
Metric Video
Yet another potentially useful type of geospatial intelligence is metric
video. Metric video allows one to obtain an object’s coordinates
by simply clicking on it. This capability is being developed [17] in
hardware and will be available to some users of some air vehicles. For
everyone else, could software and regular video from an arbitrary
UAS act as a poor man’s metric sensor? Such a solution could also
avoid costs of retrofitting existing systems.
We used the same video and the mosaic generated in the previous
concept demonstration. Each video key frame used to make the
mosaic already has a coordinate transform that warps it into mosaic
50
Tactical Geospatial Intelligence from Full Motion Video
Figure 4. Imagery orthorectified from Predator video (gray) overlaid
on an existing map (color) [10]. Pseudo-oblique view and up-todate
contents may make such maps valuable even with imperfect
orthorectification and georegistration.
coordinates, which are roughly aligned with the GPS coordinates from
the corrected telemetry. If the user clicks in one of these key frames,
the transform converts click coordinates into GPS coordinates. For
images between key frames, we reuse components of the mosaicing
algorithm to locate tie points in the image and the nearest key
frame automatically, and fit a camera rotation and translation that
best explains the motion of the tie points. This projects the clicked
coordinates from an arbitrary image to a key frame, whence they can
be converted to GPS coordinates.
In addition to detecting the coordinate of the clicked point, we
report whether the click represents a moving or stationary object.
We project the clicked image onto the mosaic and compare intensity
in the area of the click. If the mosaic and projected image have
similar intensity, then the point probably represents a stationary
object. If the two have differing intensity, the likely cause is that the
mosaic shows the typical intensity at that location and the clicked
image shows a transient object. We identify the area covered by
the transient object (the area where intensity does not match the
mosaic), and report the click as a moving object.
Figure 5 shows an example frame from a metric video. As the video
plays, clicking on the video causes a square to appear around the
click location. GPS coordinates of the clicked location appear
above the square. Green squares represent stationary objects. Their
coordinates are fixed, and their boxes are back-projected onto each
new video frame using the frame-to-mosaic transform. Red squares
represent moving objects. They are sized automatically to match
the extent of the moving object. They are visually tracked through
consecutive frames so that the red box remains on the object, not its
original location. Their changing ground coordinates are determined
from their tracked 2D coordinates using each new frame’s frameto-mosaic
transform. The mosaic, frame-to-mosaic transforms, and
the locations of moving objects are all determined in preprocessing
so that the application runs for the user at full video speed. This is
suitable for operating on archived video where the user does not see
the preprocessing time. Additional work would be required to operate
on real-time, streaming video.

Figure 5. Poor-man’s metric video. User clicks on objects, generating
boxes that lock onto the objects through later video frames, determine
whether the objects are stationary (green) or moving (red), and
report their GPS coordinates.
Video Markup
A final useful type of geospatial intelligence reverses the concept
of the metric video. A user marks up an image mosaic, and the
markings are projected into the video. This could provide a useful,
nonoverhead perspective of a battlefield with control graphics for
mission rehearsal or after-action review. Or if air assets are available
during a battle and the software were dramatically accelerated, it
could potentially provide real-time observation of how units move
relative to intended controls, as an audience currently expects for
televised sports [18].
Figure 6 shows an example. Battlefield control graphics [19] are
drawn over the mosaic from the previous demonstrations using a
paint program that supports layers. The markup layer is saved as an
image with the same coordinate system as the original mosaic. When
displaying each video frame, the frame’s frame-to-mosaic transform
is used to project the markup layer into the frame’s coordinates, and
the markings are overlaid on the image. The result is a perspective
on the control graphics that may be more intuitive to a human.
Conclusion
Tactical echelons require geospatial intelligence, such as maps and
coordinates, which could be derived from the FMV from UAS that
are now ubiquitous on the battlefield. They simply need tools to
convert the video into a more immediately useful format. We have
shown four possible tools in concept demonstrations that convert
FMV into, respectively: accurate coordinates of objects-of-interest;
intuitive, timely, orthorectified, and georegistered imagery of an
area of interest; object coordinates extractable by clicking directly
on video; and battlefield control graphics projected into the video.
We believe these applications can provide valuable geospatial
intelligence to the tactical user.
Tactical Geospatial Intelligence from Full Motion Video
Figure 6. Video markup. Control graphics are drawn over orthorectified
imagery using a paint program, then projected into video for
comparison against activities shown by the video.
51

Acknowledgment
This work was funded under Draper Laboratory’s Internal Research
and Development Program.
References
[1] 10 U.S.C. S 467: U.S. Code-Section 467: Definitions.
[2] “AGC Brochure,” http://www.agc.army.mil/publications/AGCbrochure.
pdf, July 28, 2010.
[3] Field Manual 34-130, Headquarters, Department of the Army,
July 8, 1994.
[4] “Too Much Information: Taming the UAV Data Explosion,”
http://www.defenseindustrydaily.com/uav-data-volumesolutions-06348,
March 16, 2010.
[5] Drew, C., “Drones Are Weapons of Choice in Fighting Al Qaeda,”
The New York Times, March 16, 2009.
[6] “ARGUS-IS,” http://www.darpa.mil/ipto/programs/argus/argus.
asp, July 28, 2010.
[7] “Gorgon Stare Update,” Air Force Magazine, Vol. 98, No. 5, May 2010.
[8] Baldor, L., “Air Force Develops New Sensor to Gather War Intel,” The
Seattle Times, July 6, 2009.
[9] Richards, J.E., Integrating the Army Geospatial Enterprise:
Synchronizing Geospatial-Intelligence to the Dismounted Soldier,
Master of Science in Engineering and Management Thesis, System
Design and Management Program, Massachusetts Institute of
Technology, June 2010.
[10] Madison, R., P. DeBitetto, A.R. Olean, M. Peebles, “Target Geolocation
from a Small Unmanned Aircraft System,” IEEE Aerospace
Conference, 2008.
[11] Google, “Google Earth,” http://earth.google.com, July 22, 2010.
[12] Corp., C.V., “GPS Altimetry,” http://docs.controlvision.com/pages/
gps altimetry.php, 2004.
[13] Mehaffey, J., “GPS Altitude Readout > How Accurate?” http://
gpsinformation.net/main/altitude.htm, February 10, 2001.
[14] Lowe, D.G., “Distinctive Image Features from Scale-Invariant
Keypoints,” Int. J. Comput. Vision, Vol. 60, No. 2, 2004, pp. 91-110.
[15] Xu, Y. and R. Madison, “Robust Object Recognition Using a Cascade
of Geometric Consistency Filters,” Proc. Applied Imagery and
Pattern Recognition, 2009.
[16] Page, L., S. Brin, R. Motwani, T. Winograd, The PageRank Citation
Ranking: Bringing Order to the Web, Stanford InfoLab Technical
Report.
[17] DARPA, “Standoff Precision ID in 3D (SPI-3D),” http://www.darpa.
mil/ipto/programs/spi3d/spi3d.asp, 2010.
[18] Sportvision, Inc., “SportVision,” http://www.sportvision.com/, 2008.
[19] U.S. Department of Defense, “Common Warfighting Symbology,”
MIL-STD-2525C, November 17, 2008.
52
Tactical Geospatial Intelligence from Full Motion Video

Richard W. Madison is a Senior Member of Technical Staff in the Perception Systems group at Draper Laboratory.
His work is in vision-aided navigation with forays into related fields, such as tracking, targeting, and augmenting
reality. Before joining Draper, he worked on similar projects at the Jet Propulsion Laboratory, Creative Optics, Inc.,
and the Air Force Research Laboratory. Dr. Madison holds a B.S. in Engineering from Harvey Mudd College and M.S.
and Ph.D. degrees in Electrical and Computer Engineering from Carnegie Mellon University.
Yuetian Xu is a Member of Technical Staff at Draper Laboratory. His current research interests include computer
vision, robotic navigation, GPU computing, embedded systems (Android), and biomedical imaging. Mr. Xu holds
B.S. and M.S. degrees in Electrical Engineering and Computer Science from MIT.
Tactical Geospatial Intelligence from Full Motion Video
53

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Qst2i Quatprop Qst2i_neg
The rapid development of guidance, navigation, and control (Gn&C)
systems for precision pointing and tracking spacecraft requires a set
of tools that leverages common architectural elements and a modelbased
design and implementation approach.
The paper presents an approach that can accelerate the speed of
development while reducing the cost of Gn&C flight software. It uses a
spacecraft’s pointing and tracking system as an example, and describes
the detailed models of elements such as gyros, reaction wheels, and
telescopes, as well as Gn&C algorithms and the direct conversion of
the models into software for software-in-loop and hardware-in-loop
testing.
Model-based design and software development is slowly being adopted
in the aerospace industry, but Draper is more flexible and is able to
adopt these types of time-saving techniques more quickly. Draper is
applying this approach today as a member of the Orbital Sciencesled
team competing for nASA’s Commercial Orbital Transportation
(COTS) program, as well as in the development of the ExoplanetSat
planet-finding cubesat in partnership with MIT.
K
Model-Based Design and Implementation of Pointing and Tracking Systems
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Model-Based Design and Implementation of Pointing
and Tracking Systems: From Model to Code in One Step
Sungyung Lim, Benjamin F. Lane, Bradley A. Moran, Timothy C. Henderson, and Frank A. Geisel
Copyright © 2010 American Astronautical Society (AAS), Presented at the 33rd AAS Guidance and Control Conference,
Breckenridge, CO, February 6 - 10, 2010
Abstract
This paper presents an integrated model-based design and implementation approach of pointing and tracking systems that can shorten
the design cycle and reduce the development cost of guidance, navigation, and control (GNC) flight software. It provides detailed models of
critical pointing and tracking system elements such as gyros, reaction wheels, and telescopes, as well as essential pointing and tracking GNC
algorithms. This paper describes the process of developing models and algorithms followed by direct conversion of the models into software
for software-in-the-loop (SWIL) and hardware-in-the-loop (HWIL) tests. A representative pointing system is studied to provide valuable
insights into the model-based GNC design.
Introduction
Pointing and tracking (P&T) systems are very important
elements of surveillance, strategic defense applications, optical
communications, and science observations, including both
astronomical and terrestrial targets. A P&T system is generally
required to provide both agility (the ability to rapidly change
pointing line-of-sight (LOS) vector over large angles) and jitter
suppression; the design challenge comes from trying to achieve
both in a cost-effective manner.
The Operational Responsive Space (ORS) field seeks to develop
and deploy a constellation of small and low-cost yet customized
P&T systems in a short period of time [1]. In this situation, certain
traditional practices in engineering, development, and operation
may become stumbling blocks. Reusability of heritage engineering
tools and flight software is seemingly attractive but often conceals a
high price tag. Even relatively straightforward efforts to customize
or adapt heritage flight software are often time-consuming and
costly.
A novel approach to address these challenges is model-based design
and implementation [2]. This approach can be summarized with
three steps: (1) analysis and design of algorithms with simulation
and a user-friendly language such as Matlab/Simulink, (2) automatic
code generation of flight software from algorithms written in the
modeling language, and (3) continuous validation and verification
of flight software against source models and simulation.
Although this approach does not generate all vehicle and GNC flight
software (e.g., mission manager, scheduler, and data management
are generally not included), it can significantly reduce the cycle
of the design and implementation of core GNC algorithms by
streamlining the design and implementation process. In particular,
iterative design processes are simplified as single design iteration
Model-Based Design and Implementation of Pointing and Tracking Systems
only requires modification of the algorithm blocks. Also, critical
implementation issues can be detected early, possibly even in the
algorithm development stage. They encompass accommodation of
processor speed and memory limitations, numerical representation
(floating or fixed point), and incorporation of real-time data
management such as buffering, streaming, and pipelining.
This paper presents a model-based design and implementation
approach for P&T systems. First, potential P&T elements are
surveyed. Then detailed models of key P&T elements and core GNC
algorithms are provided. Models such as reaction wheels, telescopes,
focal plane sensors, and several others are presented, together with
typical parameter ranges. Next, the GNC algorithms for spacecraft
attitude and telescope LOS stabilization are provided. Although
detailed analysis and design techniques are not addressed, critical
rules-of-thumb are provided to guide gain parameter selection.
Within the model-based design approach, the models and algorithms
are developed using Matlab/Simulink blocks and Embedded Matlab
Language (EML) so that they may be autocoded directly to flight
model and software [3]. They are partitioned into flight model
and flight software groups, respectively. This partition simplifies
implementation of SWIL and HWIL tests. Each model or algorithm
is connected to others using “rate transition blocks” [4]. The use
of rate transition blocks brings some advantages; such blocks can
be implemented at a designated rate in simulation, and the code
generated by autocoding is grouped in terms of rate. Integration
of autocoded flight software into main flight software is therefore
much easier as it needs to identify a few different rate groups and
need not identify all functions of autocoded flight software. At the
end of the paper, a GNC design example for a representative P&T
system is provided with some interesting plots and discussions.
55

Pointing and Tracking Systems
A P&T system can be roughly grouped into spacecraft elements and
payload elements. The spacecraft elements may include actuators
(reaction wheels, control momentum gyros, magnetic torque rods,
etc.), sensors (star tracker, gyro, fine guidance sensor, etc.), and
GNC flight software. Since they have been standardized with unique
roles, spacecraft elements are omitted in the discussion of this
section, but will be addressed in great detail in subsequent sections.
Payload elements may consist of a variety of components,
including imaging systems, focal plane sensors, steering mirrors,
and references. Figure 1 illustrates potential candidates for each
functional group. Since some elements have unique roles and
others have partially overlapping roles, they need to be downselected
carefully in order to derive a specific P&T architecture. The
functional groups and their associated options are briefly discussed
as follows:
• Mount mechanism: strapdown, (active or passive) vibrationisolated
optical bench, or gimbaled optical bench.
A strapdown system is simplest; the payload is mounted rigidly
to the spacecraft. A vibration-isolation optical bench eliminates
vibration coupling between the spacecraft and the payload;
such isolation can be active or passive. A gimbal mechanism
provides stable and/or agile tracking capability at the cost of
additional complexity.
• Pointing reference signal source: payload mission signal,
inertially-stable reference signal or an independent
observation signal.
A pointing reference for a tracking system is generally required.
It can come from the mission payload itself (e.g., from a targettracking
algorithm applied to the instrument focal plane data),
from a separate reference source (e.g., an inertially stabilized
laser [5]), or from a separate tracking sensor.
• Moving elements: A steering mirror may be placed as the first
optical element in the payload (“siderostat”) or later in the
optical beam-train (“fast-steering mirror” or FSM).
A siderostat provides beam agility for moving object tracking
and rapid repointing, but does not generally have the highbandwidth
motion capability required for vibration rejection. It
is also comparatively massive and costly. An FSM by contrast can
provide sufficient high-bandwidth beam steering for jitter
rejection at modest cost and complexity. However, an FSM is
generally limited by optical constraints to a relatively small
angular operating range.
56
Model-Based Design and Implementation of Pointing and Tracking Systems
• Sensing elements: focal plane array (FPA) sensor, wavefront
sensor, inertial sensors (Microelectromechanical System
(MEMS) accelerometer or gyro, high-frequency angular
rate sensor).
An FPA sensor can be used to locate a target or reference source
such as a star. The sensor generally provides very high-accuracy
information, and in particular, is not subject to significant lowfrequency
drift error. However, due to the often limited update
bandwidth (e.g., guide stars are faint, requiring long exposures),
it is often desirable to augment the FPA sensor with a highbandwidth
inertial sensor.
• Moving-element actuators: brushless DC motor, stepper motor,
piezo device (PZT).
Motors are generally used to actuate the gimbaled elements,
while PZTs are used for short-stroke high-bandwidth
applications such as FSM steering. A DC motor is typically used
for high-frequency actuation, while a stepper motor is preferred
for low-frequency actuation.
• Moving-element sensors: encoder, gyro, or reference object.
The encoder and gyro directly sense the relative angle of
gimbals. The information of known objects could enhance
sensing accuracy of encoder and gyro with measurement
uncertainty and drift.
A specific P&T system can be designed to meet a set of system
requirements by choosing among the menu of available options
and constructing models using the available elements. Two
representive examples are depicted in Figures 2 and 3. Figure 2
is an example of a “passive strapdown” P&T system in which all
pointing and tracking capability is provided by the host spacecraft.
In this case, the payload is rigidly mounted to the spacecraft and
has no active elements to control its LOS. A variant of this approach
uses the payload instrument to provide pointing information, e.g.,
by tracking a guide star in the instrument focal plane; such an
approach can eliminate the effect of misalignments between the
instrument and the spacecraft [6].
Figure 3 is an example of an “active strapdown” P&T system. The
P&T capability is shared by both the spacecraft and the payload; the
spacecraft provides large-angle agility and coarse pointing stability,
while an FSM in the payload is used to reject high-frequency and
small-amplitude pointing jitter [7], [8]. In this configuration, it is
important to manage the interaction between the spacecraft and the
payload carefully. This paper will focus mainly on this P&T system.
Note that the strapdown passive P&T system is a simplified version,
and thus, the discussion in this paper can be applied directly to
this system.

Optics
• Flexible
• Rigid
Fast Steering Mirror
• Rigid
• Flexible
Figure 1. Elements of P&T system.
Guidance
Actuators
Mirror Control
Software
OPTICAL BEnCH
SPACECRAFT
Gimbaled Siderostat
• Stepper/DC motor
• Encoder/Gyro
REF
CAMERA GYRO
BEAM
ARRAY ARRAY
PLATFORM
SERVO QUAD
SERVO
ACS Software
Rigid Body
Flex Modes
S/C Dynamics
Steering Mirror
Ref Beam Sensor
Model-Based Design and Implementation of Pointing and Tracking Systems
ECC
Figure 2. Functional block diagram of strapdown passive P&T system.
STAR
Ref beam
IMAGER
Gyros
Star Trackers
Inertial Sensors
Rigid Optics
CMD
SERVO
Pointing Reference Beam Unit
• Mechanical/MEMS Gyro
TRAnSDUCER
True state
Ref beam
True LOS
True LOS
Platform
• Vibration isolator
• Gimbal mechanism
Detector
• Focal plane
• Wave front
• Inertial
• Known object
Imager
57

Modeling of Pointing and Tracking Systems
The spacecraft of interest in this paper are small spacecraft with
masses in the 5- to 500-kg range. This class encompasses 6U
CubeSats as well as Small Explorer (SMEX) missions [6], [8], [9].
The key characteristics and approximate parameter ranges are
summarized in Table 1. This class of spacecraft typically implements
one of the two P&T system architectures introduced in the previous
section.
Spacecraft Attitude Dynamics
The spacecraft is assumed to be a rigid body with small flexible
appendages such as solar panels and structural modes at high
frequencies. The flexibility is modeled as a series of 2nd-order massspring-damper
systems, a simplified version of Craig-Bampton or
Liken’s approach [10]. The effective mass and natural frequencies
are estimated by standard NASTRAN analysis, and the damping
ratio is typically assumed to be between 0.1% and 1%. This massspring-damper
system can also be used to model a vibration
isolation mechanism or an optical bench.
Table 1. Spacecraft Parameters.
Condition Improvement
Mass (kg) 5 ~ 500
Moment of Inertia (kg-m 2 ) 0.05 ~ 100
Dimension (m 2 ) 0.2 × 0.3 ~ 1.5 × 3.0
Power (W) 30 ~ 350
Pointing Accuracy (arcsec, 3σ) 0.2 ~ 60
58
Guidance
Actuators
Mirror Control
Software
ACS Software
Rigid Body
S/C Dynamics
Steering Mirror
Ref Beam Sensor
Ref Beam
Figure 3. Functional block diagram of strapdown active P&T system.
Gyros
Star Trackers
Inertial Sensors
Rigid Optics
True LOS
True LOS
Model-Based Design and Implementation of Pointing and Tracking Systems
Imager
Spacecraft Disturbance
The spacecraft experiences known internal and environmental
disturbance during operation. The internal disturbance
encompasses reaction wheel (RW)/control momentum gyro (CMG)
torque noise, cryocooler disturbance, reaction torque of any
moving parts and/or subsystems, and thermal snap during solar
eclipse ingress/egress. For small spacecraft, reaction torques and
any dynamic interaction between spacecraft structure and periodic
internal disturbances are most important. The thermal snap must
be taken care of by system design, i.e., use of an attached or stiffened
solar array.
External torques that act on spacecraft stem from gravity
gradients, solar radiation pressure, residual magnetic dipoles, and
aerodynamic drag. Solar torque is often coupled to orbit rate and
must be considered when sizing the momentum capacity of RWs.
Since it varies seasonally, at least four seasonal values need to be
assessed. The frequency of gravity gradient variations can vary from
the orbit rate to the slew rate. The secular component of the gravity
gradient is another factor to be considered in assessing the required
momentum capacity of RWs. It is typically calculated during an
inertial pointing where the gravity gradient is maximal (45-deg
rotation along one axis that has a nonminimum moment of inertia).
The importance of aerodynamic drag decreases with increasing
altitude and is often ignored beyond 400 km [11].
Reaction Wheels
RWs provide the necessary torque for slews and disturbance compensation
via the exchange of angular momentum with the spacecraft.
Figure 4 shows the implementation of the mathematical model
developed in [12]; Table 2 gives the key parameters with a range of
typical values based on RWs for small spacecraft of interest.

1 -1 Kpu K- 1
z
Transformation scale factor RW electronics
Bus to RWs
Process Delay
KTs
z-1
Figure 4. Reaction wheel Simulink block diagram.
Table 2. Reaction wheel Parameters.
Parameters Range
Inertia (g-m 2 ) 0.01 ~ 30
Max Speed (rpm) 1000 ~ 10000
Max Momentum (mN-m-s) 1 ~ 8000
Max Torque (mN-m) 0.5 ~ 90
Process Delay (ms) 50 ~ 250
Coulomb Friction (mN-m) 0.2 ~ 2
Quantization Error (mN-m) 0.002 ~ 0.005
Random Noise (mN-m, 1σ) 0.03 ~ 0.05
Angular Torque Noise (deg) ~10
Static Imbalance (mg-cm) 2 ~ 500
Dynamic Imbalance (mg-cm 2 ) 20 ~ 5000
The literature [13] primarily focuses on static and dynamic
imbalances in RWs and their influence on spacecraft pointing.
However, for small spacecraft with pointing accuracy down to
arcsec levels, the effects of processor delay, quantization error,
angular torque noise, and RW speed zero-crossing are equal to or
K-
3pN sin
quantization
error
SC_Effect_RW_VisFrict
Model-Based Design and Implementation of Pointing and Tracking Systems
K-
coeff
SC_Effect_RW_CoulFrict
Coulomb Friction
×
×
×
+
+
+
+
Angular torque noise
torque noise
+
+
+
-
SC_Effector_RW_InitSpeed
Initial RW speed
K
max torque limit Transformation
RWs to Bus
pu u 2
KTs
z-1
K-
SC_RW_Us * SC_RW_distFromCG
Static imblance Const
SC_RW_Ud
KTs
x
oz-1 sin
cos
K-
×
×
K p u
+ +
1
rwTrq
more important than those of static and dynamic imbalance. At
that level of performance, due to unfavorable inertia ratios and
inexpensive components, everything matters.
Processor delay is determined by the GNC computer command
output rate and the rate of the RW motor processor. As pointed
out in the literature [12], angular torque noise is a critical RW
noise source as its frequency is often close to the bandwidth of
the spacecraft GNC controller. However, the effect of this torque
can be mitigated significantly using a high-bandwidth spacecraft
GNC controller.
Control Momentum Gyros
Miniature CMGs have been developed for small spacecraft [14]. One
example produces a torque of 52.25 mN-m, sufficient to generate an
average slew of 3 deg/s. It also consumes 20-70% less power than
RWs of the same weight. However, the static imbalance torque at
the fundamental frequency is larger by an order of magnitude than
that of RWs with similar maximum torque capability. Furthermore,
bearing lifetime is an important issue since CMGs are typically
spinning as fast as 11 krpm. These reasons tend to make RWs
preferred as primary actuators for P&T systems as long as pointing
stability is more important than a fast slewing capability.
Simultaneous pointing stability and fast tracking are difficult to
achieve by either RWs or CMGs. The use of both actuators may
be required at the cost of increased mass and complexity. Alternative
solutions involve either using FSMs or gimbals to articulate the
entire payload or siderostat to actuate the payload LOS
vector [15], [16].
×
×
×
×
+
+
+
+
K p u
2
3
K p u
+
+
59

Star Tracker
The star tracker (ST) is one of the major attitude determination
instruments for spacecraft. Miniaturized STs with low mass and
power consumption are recently preferred for small spacecraft.
Even a low-end, compact ST with limited accuracy (e.g., 18-90
arcsec) is in high demand for autonomous attitude determination
of CubeSat spacecraft (mass 200 Hz.
However, it is typically reduced (averaged) to an effective rate of
20 Hz or less in order to reduce the effect of angle white noise. Bias
stability, which is a random process, is generally a more important
factor in determining navigation filter performance than is pure
bias, especially when the time constant of the bias stability is
relatively short.
A gyro can also be used to measure the local attitude of the payload,
which can be different from spacecraft attitude determination.
Examples encompass attitude measurement of packaged P&T
elements such as the “inertial pseudostar reference unit” [5]
and attitude measurement of a gimbaled siderostat. In the latter
situation, the gyro effectively replaces an encoder.
sqt
×
q qnorm
Qnorm2
Qb2c
Qa2c q qnorm Qa2b Qb2b
Qa2b Qnorm Qpose
Qmult
×
1
Qst2i

SC_Sensor_Gyro_Qb2gref
Table 4. Gyro Parameters.
Parameters Range
Output Rate (Hz) 100 ~ 200
Angle Random Walk (deg/√h) 0.00015 ~ 0.1
Angle White Noise
(arcsec/√Hz)
Rate Random Walk
(arcsec/√s 3 )
1
wb
Figure 6. Gyro Simulink block diagram.
>0.0035
>9.495E-5
Bias Stability (deg/h) 0.0045 ~ 3.3
qi2b
QtransVect
Bias Stability Time Constant (s) ~300
SC_Sensor_Gyro_Bias
Transformation
matrix
nonorthogonality
Scale Factor (ppm) 1 ~ 100
vi
rate random work
bias stability
Telescope
A telescope generally compresses beams of light and focuses them
onto an FPA detector. A model based on geometric ray-trace optics
was developed [15] and implemented using Matlab/Simulink
blocks and EML. This formalism provides a way to derive (to linear
order) the effect of motion of optical system components on
the focal plane image. Thus, it becomes possible to model effects
ranging from deliberate actuation or pointing of a body-fixed largeaperture
telescope, through the motion of a siderostat, to the motion
of a small FSM. It is also possible to include rigid-body motions
of the optical elements themselves due to effects such as
spacecraft vibration.
The model is initialized with an optical prescription specifying the
parameters of the imaging system, including mirror dimensions,
Model-Based Design and Implementation of Pointing and Tracking Systems
K Ts
z -1
(n)=Cx(n)+Du(n)
(n+1)=Ax(n)+Bu
1st MP with time cost
vb Kxu 0.5
+
+ + -K-
+
+
+
+
1
z
prev sample1
+
+
+
angle random
walk
K-
scale
factor
conic constants, placement, and orientation. Some key parameters
of a representative Ritchey-Chretien telescope with f/D = 6.9
are listed in Table 5 [22]. The model can directly calculate the
combined focal plane spot position of input beam angle and
position in simulation. Furthermore, it can also drive sensitivity
matrices relating input beam angle and position to focal plane spot
position. For example, the chief ray of the representative telescope
model has the following relationship:
dU
dV
dt
to derive delta angle
angle white noise
+ +
dφ
3.45 0 0
= dθ +
0 3.45 0
1
-K- 1
1/dt
to derive
rate signals
-0.08 0
0 -0.08
dx
dy
where [dU, dV] are focal plane spot position changes in meters, [dφ,
dθ] are input beam angle changes in radians, and [dx, dy] are FSM
angles in radians. This is essentially a simple pinhole model of star
tracker or camera modified to include an FSM, however, higherfidelity
models can be incorporated with ease.
A particularly useful feature of the adopted model is that it
accurately accounts for optical effects such as beam walk as well
as certain types of optical aberrations of an active P&T system. In
particular, consider a strapdown active P&T system: a small FSM
is used at high bandwidth to correct for small-angle errors, while
the spacecraft is operated so as to keep the FSM centered. This
configuration was used in the Joint Astrophysics Nascent Universe
Satellite (JANUS) and James Webb Space Telescope (JWST) [7], [8].
However, such an FSM is sometimes limited by the fact that the FSM
is not always placed in the system pupil. As a consequence, as the
field of view of an imaging system is increased, the nonideal FSM
location results in additional blurring, which is a function of the
magnitude of the spacecraft pointing error. By accurately modeling
the optical system in its entirety, it is possible to accurately derive
Out
(1)
61

Table 5. Representative Ritchey-Chretien Telescope Parameters.
Parameters Value
Dimension (m 2 ) 0.5 × 0.9
Primary Focal Length (m) 1.0
Effective Focal Length (m) 3.45
Distance from Primary Mirror to System
Focal Point (m)
Distance between Primary Mirror and
Secondary Mirror (m)
the coupling between spacecraft body pointing stability and image
quality, and thus perform better system-level trades during the GNC
design process.
Focal Plane Array Sensor
A simple FPA model was developed using Matlab/Simulink blocks
and EML to simulate the effects of realistic detector integration,
pixilation, and detector noise. In addition, the effects of diffraction,
while not modeled accurately in the geometric ray-trace approach
outlined above, can be accounted for by convolving the resulting
detector image with a suitable point-spread function (PSF). Some
key parameters of a representative FPA detector are listed in Table
6 [22].
Table 6. Representative Focal Plane Array Sensor Parameter.
Parameters Value
Star Flux at Magnitude Zero Point (photons/cm 2 /s) 1.26E+6
Dark Current & Detector Background Noise (e/p/s) 5.0
Detector Readout Noise (electrons) 20
Pixel Size (μm) 10
Effective Noise (arcsec, 1σ) 0.1
The detector noise model includes terms for detector dark current,
read noise, and scattered light background as well as photon noise.
Signal levels (photon count rates) are determined by integrating
suitable stellar spectral templates multiplied by detector response
functions and mirror coating reflectivity [22].
A new class of FPA sensor has recently become available from
Teledyne [23], the HAWAII-2RG detector, which is designed to allow
simultaneous multiple readouts of different locations on the chip
at different rates. Thus, it is possible to read out a small “guide box”
of 10-20 pixels on a side, typically centered on a bright star, at a
rapid rate (~10 Hz) while reading out the remaining pixels of the
4096 × 4096 array at a much slower rate for increased sensitivity.
62
0.6
0.73
Eccentricity of Primary Mirror 1.23
Eccentricity of Secondary Mirror 1.74
Model-Based Design and Implementation of Pointing and Tracking Systems
Thus, it becomes possible to use the same focal plane both as a fine
guidance sensor and as a science detector, greatly simplifying the
optical system and eliminating the need for a second focal plane
array devoted entirely to instrument guiding [23].
The post-detection signal processing is also modeled. Such
processing takes the simulated image and passes it to a star
detection algorithm that compares the magnitude of the candidate
star to the detector noise level; once the signal-to-noise ratio
exceeds a set threshold, a “star present” flag is set to “ON,” and the
position of the detected star is derived using a simple centroiding
algorithm. This information is then passed to the spacecraft and/or
the payload GNC loop.
Angular Rate Sensor
An angular rate sensor (ARS) senses high-frequency angular
vibration. It accurately detects vibrations with frequencies above 10
Hz as well as vibrations in the 1- to 10-Hz range with some degraded
performance. In this range, a simple logic to compensate for known
gain and phase loss may improve the controller performance.
Recently, some efforts have been made to replace and/or enhance an
ARS with MEMS accelerometers for the detection of high-frequency
vibration [24]. The ARS sensor can be simply represented by a 2ndorder
high-pass filter as follows:
s(s + 10)
s 2 + (4p)s + (4p) 2
The typical random noise of ARS is 8 μrad/s (1σ) and the range is
10 rad/s.
Others
We have also developed models for such elements as MEMS
accelerometers, magnetic torque rods and magnetometers, gimbal
mechanisms, motors, and FSM mechanisms.
GNC Algorithms for Pointing and Tracking Systems
The choice of a P&T GNC algorithm depends on the specific
architecture under investigation. We are focusing on the strapdown
active P&T system described before. In this case, the algorithm can
be grouped into the spacecraft GNC algorithm and the payload
GNC algorithm. The spacecraft GNC algorithm slews the spacecraft
toward a designated target and stabilizes the spacecraft attitude
around the target using reaction wheels, star tracker, and gyro. The
payload GNC algorithm precisely stabilizes the LOS vector of the
payload to the target using the FSM, FPA detector, and the ARS.
Note that only essential GNC algorithms are addressed in this paper.
For example, less critical algorithms such as the RW momentum
control loop using magnetic torque rods are omitted for brevity.
Spacecraft GNC Algorithm
The spacecraft GNC algorithm consists of four major components:
slew maneuver planner, navigation filter, sensor processing, and
control law, each implemented using Matlab/Simulink blocks and
EML. The Attitude Determination and Control System (ADCS)
Mission Manager and FSW Scheduler are not parts of typical GNC
algorithms, but rather are functions of an upper-level program that
integrates and executes these tasks. They are typically developed by
GNC software engineers directly in C or C++ [25]. However, since
(2)

they are prerequisite for implementing GNC algorithms, simplified
versions are modeled to a limited extent. The ADCS Mission
Manager can read predefined time-tagged mission profile data and
command slew instructions and control modes; the FSW Scheduler
is not explicitly modeled but implicitly using “rate transition
blocks” that allow the GNC algorithms to be executed in specific
execution cycles.
The Slew Maneuver Planner processes slew information and
provides smooth spinup-coasting-spindown eigenrotation attitude
profiles along slew directions defined in the slew command. The
attitude profile includes commanded quaternion and angular rate.
The Slew Maneuver Planner can employ an advanced slew algorithm
that can provide the shortest slew profile even when Earth, Sun, and
Moon are in the path of eigenrotation slewing.
The Navigation Filter consists of three major components:
compensation, extended Kalman navigation filter (KF), and error
calculation. The Compensation compensates for gyro bias in the
measured gyro data using an estimated value from the navigation
filter and compensates for deterministic time delay in the measured
attitude quaternion using the measured gyro rate data. The Error
Calculation calculates the error state of attitude and rate for the
1
wbG
averager
1
z
wbGds
wbG
SC_Ctrl_eKF_M
SC_Ctrl_eKF_H
SC_Ctrl_eKF_R
SC_Ctrl_N_Its
SC_Sensor_Gyro_BS_tau
2
Qst2i
SC_Ctrl_N_Its
Figure 7. Extended Kalman navigation filter Simulink block diagram.
Model-Based Design and Implementation of Pointing and Tracking Systems
w
wp
M
H
R
delT
tau
stMeasOn
Qst2i Quatprop Qst2i_neg
delT
eKFprop
K
Control Law and estimates the gyroscopic term, which is not
negligible in fast slewing.
The most complicated component is the extended Kalman
navigation filter depicted in Figure 7. This block implements a
standard extended KF that processes the measured rate and attitude
quaternions and produces the rate and attitude quaternion states
[26]. It has three components: quaternion propagation, Kalman gain
propagation (eKFprop), and filter state update. As shown in Figure
7, three “rate transition blocks” are specially employed around the
eKFprop block. The main purpose is to allow the eKFprop block
to be executed at a different rate from the others (e.g., 1 Hz). The
eKFprop block executes standard matrix calculations, including
matrix inversion, which are often computationally expensive
elements. By running this block at a lower rate, the computational
load on the flight computer may be reduced, albeit at a cost of
reduced performance of the extended KF.
The Control Law implements a simple proportional and derivative
(PD) controller. The gains are parameterized in terms of damping
ratio and bandwidth as follows:
Kw = 2ξw J Kp = w N 2
NJ (3)
1
z
K
Qst2i_neg
Qst2i
update
bias_pos
Qst2i_pos
2
wbE
1
wb_biasE
3
Qst2iE
63

Here, J is the moment of inertia. The damping ratio (ξ) is typically
0.707 in most applications, but a high damping ratio (e.g., 0.995)
is recommended in the P&T application to minimize undesired
overshoot. The bandwidth (w ) is typically gain-scheduled by
N
a function of active mode (i.e., fast slew mode and fine tracking
mode) and active status of payload GNC system. For example, a high
bandwidth is used for the spacecraft GNC controller during fast slew
mode, while a low bandwidth is used during fine tracking mode.
When the payload GNC system is active, the bandwidth for the
spacecraft GNC controller tends to be lowered further to prevent
the two GNC loops from fighting each other.
The bandwidth is a critical element to pointing accuracy. A higher
bandwidth yields better pointing accuracy, or equivalently, smaller
pointing error. Therefore, it is typically asked what the nominal
bandwidth is and what the achievable bandwidth is. This is
another reason why we prefer to employ the simple PD controller
characterized by bandwidth instead of more advanced controllers
such as linear quadratic Gaussian (LQG) and H-infinity.
Without detailed analysis such as stability and Monte Carlo
analysis, a typical range of bandwidth could be estimated by a
rule-of-thumb waterfall effect—an order reduction in magnitude
at each step. In particular, reduction in bandwidth happens at the
step from GNC process cycle to GNC navigation filter bandwidth,
and another reduction follows at the step from GNC navigation filter
bandwidth to GNC controller bandwidth. Of course, the magnitude
of reduction may vary around 10% as a function of the quality of
the GNC subsystems. For example, using a very high-end gyro like
an SIRU makes it possible for GNC controller bandwidth to be 2/10
of navigation filter bandwidth.
Payload GNC Algorithm
The payload GNC algorithm is typically simple and effectively
consists of only the control algorithm as shown in Figure 8. The
64
2
ARS_rate
FPA_uv
1
e_ARS_HPF
e_ARS_HPF
e_ARS_HPF
e_ARS_HPF
e_ARS_HPF
e_ARS_HPF
K Ts
z-1
K p u
P gain
K p u
I gain
Integrator
BeamTF
InvFSMOpticalGain
Figure 8. Simplified P&T payload GnC Simulink block diagram.
1
s
+
+
K p u
Model-Based Design and Implementation of Pointing and Tracking Systems
main reason for making the payload GNC flight software as simple
as possible is that it often runs at a high rate (particularly if it
incorporates a high-rate FSM) on a field-programmable gate array
(FPGA) with limited computational resources.
The control algorithm stabilizes the location of the image of a
guidance target (e.g., photons from a star) on the focal plane with
the FSM tilt angle modulated by measurements of the FPA detector
and the ARS. The FSM tilt angle loop consists of an ARS-to-FSM loop
and an FPA-to-FSM loop. The former compensates for the image
jitter with frequency higher than 5 Hz and the latter compensates
for the image jitter with frequency lower than 5 Hz. To ensure that
there is no frequency overlapping, the ARS-to-FSM loop employs
washout filters.
The FPA-to-FSM loop is assumed to be a 200-Hz process. This loop
employs a proportional and integral (PI) controller. The integrator
is used for rejecting any bias component. The proportional gain is
selected to be a fraction of the ratio of FPA sensor output rate (e.g.,
10 Hz) with respect to the GNC process cycle (e.g., 200 Hz). This
is one way to generate a high-rate command signal from a low-rate
measurement. Another way is to employ a low-pass filter. The output
from the PI controller, which is the desired relative FSM tilt angle
with respect to the current one, is integrated before being combined
with the FSM command from ARS-to-FSM loop.
We have focused primarily on the pointing GNC algorithm in this
paper. However, the tracking GNC algorithm presents uniquely
challenging issues, including coordinated tracking among multiple
actuation elements of the spacecraft, payload gimbal mechanisms,
and FSM; and reconstruction of the true LOS vector from the payload
or spacecraft to a target from multiple sensor measurements such
as encoders, gyros, FPA detectors, and known reference object
locations. The development of precision tracking GNC algorithms is
one of our next research topics.
K p u
InvFSMOpticalGain1
K p u -1
+
+
1
z
+
+
1
fsmCmd

Automatic Code Generation and Implementation
We have discussed models of critical P&T elements and GNC
algorithms. Here, we shift our focus to autogeneration of flight
models and GNC flight software from the models and algorithms
discussed previously. The code generation requires a few
prerequisite conditions in the framework of model-based design.
First, the model-based design tool is constructed with appropriate
partitions that can accommodate the goal of code generation. For
example, consider a SWIL/HWIL test of spacecraft GNC algorithms.
An ideal partition consists of two main components: one for
spacecraft GNC algorithms and the other for the remaining models
sunhc
6
9
8
6
5
4
7
mode
1
tsmModeCmd
2
rmMCmd
3
rwTCmd
mode
fet
TFPA_Sensor
fuvP
fsmModelCmd
fpaBias
mode
ara2fsmC
wobA
fsmP
TFSM_FSW
fpa2fsmC
ewDone
SC_Actuators
rwSpd
rwTCmd
rwTrq
Hrw
mtMCmd
mtM
fuvP
mode
1
tsmPos
2
magBfield
3
raleGyro
4
Q_s [2]
5
10
slewDone
Figure 9. Spacecraft non-GnC flight software block.
Qt2i
HRate Sensor Proc
wbGF wb G
rwTrq
Qb2i
Hrw
mtM
wb
scPos
acc
Qt2i
fet
Bb
SC_Dynamics
mode
Qt2i
Qob2i
fuv
fsmP
fpaBias
fuvP
TOPT_Model
FSM_Dynamics
ars2fsmC
fsmP
fpa2smC
Model-Based Design and Implementation of Pointing and Tracking Systems
and algorithms. These blocks are connected by input/output signals
with “rate transition blocks” (See Figure 9). As mentioned before,
a “rate transition block” permits one to execute the algorithms at
different rates in the level of design and implementation. Second,
models and algorithms are developed using autocodable Matlab/
Simulink blocks and EML. Third, all parameters, including static
and tunable parameters such as GNC control gains, are defined as
external constant inputs to the GNC algorithm blocks and therefore
can be provided by the GNC mission manager during each specific
mission phase.
SC_Sensors
magBfield
Bb
wob
rateGyro
Qob2i
quat_st2i
rwSpd
rwSpeed
fet
Qb2i
wb
Qob2i
TFSM_PSensor
Outputs
acc
wob
wob wobA
OB_Dynamics TARS_Model
fsmP
fsmPos
65

With these prerequisite conditions, code generation is rather
straightforward using the Real-Time Workshop code generator [27].
The generated code is pre-tested with a set of regression tests before
SWIL and HWIL tests. First, it is validated and verified against the
original model or algorithm within the same model-based design
framework, using the Matlab/Simulink Verification and Validation
toolbox [28]. Second, it is tested for runtime errors using Matlab/
PolySpace [29], and further tested on a virtual process that mimics
the major functionality of a real target processor, i.e., with help of a
third-party vendor’s MULTI [30].
The code that passes the above regression tests is now ready for SWIL
and HWIL tests. For a SWIL test, the flight model code and the GNC
flight software are running separately on two Power PC processors
via internet communication using the Matlab/xPCTarget operating
system [31]. This test is useful to check communication between
flight software and the outside world and for further code validation.
For the HWIL test, the flight models can be replaced by actual flight
hardware, and the GNC flight software is running on the actual flight
computer. This test is useful to evaluate the computational speed
and memory limitations of the flight processor and data interfaces
between hardware and flight software. Furthermore, it can evaluate
network delays and communication data drop-offs. As a result, the
development, validation, and verification of flight software may be
sped up using the model-based design and implementation.
Examples
Some results are present from the strapdown active P&T system that
we have focused on, with a special focus on GNC system design and
analysis. The gains for spacecraft and payload GNC algorithms are
selected according to the guidelines described in previous sections.
The simulation executed a mission plan that sequentially executed
initial tracking, slew maneuver, and fine tracking modes.
The results are plotted in Figures 10-13. Figure 10 plots the position
error of the image with respect to the center of the FPA detector.
For convenience, the position error is converted to the LOS error,
dividing it by the effective focal length of telescope. As shown, the
image position error is reduced significantly within the threshold
of 0.35 arcsec (3σ) during fine tracking mode when the FPA
detects a guide star and provides the FSM loop with the tracking
information of that guide star. By contrast, large errors occur when
the FPA detector is in a loss-of-lock situation due to a large slew or
small repointing of the instrument LOS (“dither,” done to provide a
background calibration for the science instrument being modeled).
The large error between 100 and 250 s is due to a large spacecraft
slew. Two smaller errors with magnitudes of 5 arcsec are due to
dithering the FSM. During each dither, the FPA acquires a new guide
star and tracks it. Dithering is clearly shown in Figure 11.
Figures 12 and 13 both show power spectral densities of spacecraft
pointing LOS error, FSM tilt angle, and FPA image LOS error. In Figure
12, the spacecraft pointing error is shown to be much larger than
the requirement. This implies that a “passive P&T system” with same
spacecraft GNC system cannot meet the P&T LOS requirement.
However, as the FSM tilt angle is modulated to compensate for the
spacecraft LOS error, the image LOS error on the FPA detector is
controlled within the requirement of 0.35 arcsec (9% margin). The
same figure also shows the bandwidths of important subsystems:
66
Model-Based Design and Implementation of Pointing and Tracking Systems
the bandwidth of the spacecraft GNC system is about 0.06 Hz; the
bandwidth of the telescope GNC system is 2 Hz; the RW speed is
nominally 1200 rpm (or 20 Hz); and first solar array bending and
cryocooler frequencies are 35 Hz and 50 Hz, respectively.
Figure 13 shows how the FPA-to-FSM loop and the ARS-to-FSM loop
contribute to the power spectral density of the FSM tilt angles shown
in Figure 12. The FPA-to-FSM loop is shown to compensate for the
spacecraft pointing error with frequencies of up to 0.3 Hz (i.e.,
spacecraft response due to RW and ST noises), whereas the ARS-to-
FSM loop is shown to compensate for the spacecraft pointing error
with frequencies higher than 10 Hz (i.e., spacecraft vibration). This
is consistent with expectations based on the FPA detector and ARS
sensor bandwidths.
Conclusions
This paper has presented a model-based design and implementation
approach for pointing and tracking systems. The GNC models and
algorithms developed are comparable to heritage counterparts in
terms of accuracy and performance. Furthermore, development
and modification/adaptation of GNC flight software are time
and cost efficient, which is critical to new demands arising in the
operationally responsive space field.

Magnitude (arcsec)
Figure 10. FPA image LOS error during initial tracking, slew
maneuver, fine tracking modes.
Angle PSD (arcsec 2 /Hz)
10 6
10 5
10 4
10 3
10 2
10 1
10 0
10 -1
FPA Image Radial Error
FPA provides no data so spacecraft LOS error
is used instead
FPA acquisition is done and FPA
measurement is used to close FSM loop
10 -2
0 100 200 300 400 500 600 700 800
Time (s)
10 4
10 2
10 0
10 -2
10 -4
10 -6
FMS is dithered and FPA provides no measurement
during reacquisition. The peak is
fictitious error to represent this process
10 -8
10 -2 10 -1 10 0 10 1 10 2
Frequency (Hz)
SC Pointing Error
FPA Image Radial Error
FSM Tilt Angle
Image RMS
Figure 12. Power spectral density distribution of spacecraft LOS
error, FPA image LOS error, FSM tilt angle, and image rms.
Model-Based Design and Implementation of Pointing and Tracking Systems
Magnitude (arcsec)
20
10
0
-10
-20
-30
-40
-50
-60
-70
FSM Tilt Angle
Dithering of 5-arcsec LOS change
-80 0 100 200 300 400 500 600 700 800
Time (s)
Figure 11. FSM tilt angle during initial tracking, slew maneuver, fine
tracking modes.
Angle PSD (arcsec 2 /Hz)
10 4
10 2
10 0
10 -2
10 -4
10 -6
SC Pointing Error
(FPA, ARS)-to-FSM Tilt Angle
FPA-to-FSM Tilt Angle
ASR-to-FSM Tilt Angle
10-8 10-2 10-1 100 101 102 Frequency (Hz)
Figure 13. Power spectral density distribution of spacecraft LOS
error, combined FSM tilt angle, FPA contribution on FSM tilt angle,
and ARS contribution on FMS tilt angle.
67

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December 1999.

Sungyung Lim is a Senior Member of the Technical Staff in the Strategic and Space Guidance and Control group
at Draper Laboratory. Before joining Draper, he was a Senior Engineering Specialist at Space Systems/Loral. His
work there involved the analysis of spacecraft dynamics, on-orbit anomaly investigation of spacecraft control
systems, and the design and analysis of spacecraft pointing systems. At Draper, his work has extended to analysis
and design of GN&C algorithm and software in the strategic and space area. His current interests include modelbased
GN&C design and analysis and design of high precision pointing systems for small satellites. Dr. Lim received
B.S. and M.S. degrees in Aerospace from Seoul National University and a Ph.D. in Aeronautics and Astronautics
from Stanford University.
Benjamin F. Lane is a Senior Member of the Technical Staff at Draper Laboratory and is currently the Task
Lead for the Guidance System Concepts effort. Expertise includes the development of advanced algorithms
for image processing and real-time control systems, including adaptive optics and spacecraft instrumentation.
He has developed instrument concepts, requirements, designs, control software, integration, testing and
commissioning, and operations, debugging, and data acquisition. He helped design, build, and operate a multipleaperture
telescope system (the Palomar Testbed Interferometer) for extremely high-angular resolution (picorad)
astronomical observations, and also designed and built high-contrast imaging payloads for sounding rocket
missions and spacecraft. He has published more than 45 peer-reviewed papers in his area of expertise and is a
recipient of the 2010 Draper Excellence in Innovation Award. Dr. Lane holds a Ph.D. in Planetary Science from the
California Institute of Technology.
Bradley A. Moran is a Program Manager for Space Systems at Draper Laboratory. With 26 years of professional
experience in both academic and industry settings, he has developed and implemented GN&C algorithms
for a number of platforms ranging from undersea to on-orbit. Recent experiences include mission design and
analysis for rendezvous and proximity operations and systems engineering for NASA, DoD, and other government
sponsors. Since 2009, he has been the Mission System Engineer for the Navy’s Joint Milli-Arcsecond Pathfinder
Survey program.
Timothy C. Henderson is a Distinguished Member of the Technical Staff at Draper Laboratory. He has over
35 years of experience leading projects in structural dynamics, GN&C flight software, fault-tolerant systems,
robotics, and precision pointing and tracking. He served as Technical Director and the Attitude Determination
and Control System (ADCS) Lead for the Joint Astrophysics Nascent Universe Satellite (JANUS) Small Explorer
satellite program. He holds B.S. and M.S. degrees in Civil Engineering from Tufts University and MIT, respectively.
Frank A. Geisel is currently Program Manager for Strategic Business Development at Draper Laboratory and is
responsible for the identification, capture, and management of programs that are focused on developing leadingedge
solutions for DoD, NASA, and the Intelligence Community. He has worked on various aspects of systems
engineering, networking, and communications architectures at Draper since 2000, and has held management
positions at Draper in both the programs and engineering organizations. His early career was spent in the offshore
industry, developing and fielding deep-water integrated navigation systems and closed-loop robotic control
systems for subsea inspection, operation, and recovery applications. In the early 1980s, he was the Program
Manager for 13 major expeditions to the Arctic and Antarctic in support of oil and gas exploration. Mr. Geisel is a
Member of the AIAA, IEEE, and the Society of Naval Architects and Marine Engineers (SNAME). He received a B.S.
in Ocean Engineering from MIT.
Model-Based Design and Implementation of Pointing and Tracking Systems
69

70
Law enforcement and other security-related personnel could benefit
greatly from the ability to objectively and quantitatively determine whether
or not an individual is being deceptive during an interview.
Draper engineers, working with MRAC, have been developing ways
to detect deception during interviews using multiple, synchronized
physiological measurements. Their work attempts to bring engineering
rigor and approaches to the collection and analysis of physiological
measurements during a highly controlled psychological experiment. Most
previous research has been performed with fewer sensing modalities
in more academic environments, primarily with university students
as participants in a mock crime. This effort was the first of its kind at
Draper and represents an early step forward in exploiting physiological
measurements. Future efforts would build on this one and aim toward
developing and testing a usable tool.
The impact of this work could be valuable in any environment where two
persons interact and where the assessment of credibility of information
is important. This includes law enforcement, homeland security, and
intelligence community applications. Such knowledge could ultimately
enable better ways to elicit and validate the information from people
during interviews and could have applications in a wide variety of contexts.
The researchers continue to investigate how to quantify the physiological
responses associated with human interactions in order to make useful
inferences about intent and behavior.
Detection of Deception in Structured Interviews Using Sensors and Algorithms

Detection of Deception in Structured Interviews
Using Sensors and Algorithms
Meredith G. Cunha, Alissa C. Clarke, Jennifer Z. Martin, Jason R. Beauregard, Andrea K. Webb, Asher A.
Hensley, Nirmal Keshava, and Daniel J. Martin
Copyright © 2010 by the Society of Photo-Optical Instrumentation Engineers (SPIE). Presented at SPIE Defense, Security, and
Sensing 2010, Orlando FL, April 5-9, 2010
Abstract
Draper Laboratory and Martin Research and Consulting, LLC * (MRAC) have recently completed a comprehensive study to quantitatively
evaluate deception detection performance under different interviewing styles. The interviews were performed while multiple
physiological waveforms were collected from participants to determine how well automated algorithms can detect deception based on
changes in physiology. We report the results of a multifactorial experiment with 77 human participants who were deceptive on specific
topics during interviews conducted with one of two styles: a forcing style that relies on more coercive or confrontational techniques, or
a fostering approach that relies on open-ended interviewing and elements of a cognitive interview. The interviews were performed in a
state-of-the-art facility where multiple sensors simultaneously collect synchronized physiological measurements, including electrodermal
response, relative blood pressure, respiration, pupil diameter, and electrocardiogram (ECG). Features extracted from these waveforms
during honest and deceptive intervals were then submitted to a hypothesis test to evaluate their statistical significance. A univariate
statistical detection algorithm then assessed the ability to detect deception for different interview configurations. Our paper will explain
the protocol and experimental design for this study. Our results will be in terms of statistical significances, effect sizes, and receiver
operating characteristic (ROC) curves and will identify how promising features performed in different interview scenarios.
* MRAC is a veteran-owned research and consulting firm that specializes in bridging the gap between empirical knowledge and corporate or government applications. MRAC
conducts human subject testing for government agencies, academic institutions, and corporate industries nationwide.
Introduction
Motivation
A significant amount of current research has been focused on
detecting deception based on changes in human physiology, with
the obvious benefits to military operations, counterintelligence,
and homeland defense applications, which must optimally collect
and use human intelligence (HUMINT). Progress in this area has
largely focused on how individual sensors (e.g., functional magnetic
resonance imaging (fMRI), video, ECG) can reveal evidence of
deception, with the hope that a computerized system may be able
to automate deception detection reliably. For practical applications
outside the laboratory environment, however, an equally important
and complementary aspect is the way that information can best
be “educed” during elicitations, debriefings, and interrogations.
Thus far, however, there has been little investigation into how
sensing technologies can complement and improve on educing
methodologies and traditional observer-based credibility
assessments.
In 2006, the Intelligence Science Board published a comprehensive
report on educing information [1]. A major finding of the report
is that there has been minimal research on most methods of
educing information, and there is no scientific evidence that the
techniques commonly taught to interviewers achieve the intended
results. Indeed, it appears that there have not been any systematic
investigations of educing methodologies for almost 50 years.
According to the report, most educing tactics and procedures
Detection of Deception in Structured Interviews Using Sensors and Algorithms
taught at the various service schools have had little or no validation
and are frequently based more on casual empiricism and tradition
than on science.
In this paper, we report quantitative results of a comprehensive
study whose objective was to determine how well measurements of
physiology from multiple sensors can be collected and fused to detect
deception and to explore how two distinctly different interviewing
styles can affect deception detection (DD) performance. As part of
a broader research goal at Draper Laboratory to understand how
contact and remote sensors can be employed to infer identity and
cognitive state, the experiment was conducted in a state-of-the-art
facility that hosts multiple sensors in a monitored environment that
enables highly calibrated and synchronized measurements to be
collected and fused. Typical research facilities in this field do not
have the resources to collect synchronized data from the number of
sensors we have utilized.
Psychological Basis for Investigation
Our work builds on longstanding efforts in the area of
psychophysiology that have attempted to translate cognitive
processes attributed to stress and deception to physiological
observables [2]-[10] whose attributes have been quantified using
statistics. A key contribution of our effort has been to implement
psychophysiological features identified by different researchers
into a common, flexible analytical framework that allows their
efficacy to be impartially compared and scrutinized.
71

During the previous century, credibility assessment and deception
detection have been investigated in various scientific disciplines
including psychiatry, psychology, physiology, philosophy,
communications, and linguistics [11], [12]. Areas of specific
exploration include nonverbal behavioral cues to deception [13],
[14], verbal cues including statement validity analysis [15], [16],
psychophysiological measures of lie detection [17], [18], and the
effectiveness of training programs in detecting deception [19],
[20]. While much progress has been made in determining specific
measures that are helpful in detecting deception, research has
consistently shown that “catching liars” is a difficult task and that
people, including professional lie-catchers, often make mistakes
[12], [21]. Thus far, however, there has been little investigation
into how sensing technologies can complement and improve on
educing methodologies and traditional observer-based credibility
assessments.
Traditional psychophysiological measures used to detect deception
(collectively referred to as the polygraph or lie detector test) have
changed little since they first became available. Psychophysiological
assessment involves fitting an individual with sensors that are then
connected to a polygraph machine. These sensors measure sweating
from the palm of the hand or fingers (referred to as electrodermal
response or galvanic skin response), relative blood pressure
(measured by an inflated cuff on the upper arm), and respiration.
Recently, however, other sensors have been proposed [22] as
possible alternatives to the polygraph, such as thermal imaging, eye
tracking, or a reduced set of only two sensors (electrodermal activity
and plethysmograph, referred to as the Preliminary Credibility
Assessment Screening System (PCASS)). It now remains to be seen
to what extent these sensors will improve on current credibility
assessment methodologies.
Methods
Experimental Design
In this paper, we report quantitative results of a comprehensive
study whose objective was to determine how well measurements
of physiology from multiple sensors can be collected and fused
to detect deception and to explore how two distinctly different
interviewing styles can affect deception detection performance.
Participants were recruited primarily through an advertisement
in the Boston Metro, a free newspaper distributed in major metro
stations. Seventy-eight participants ultimately completed the study;
they were on average 42 years old and had an average of 14 years
of education. Participants were informed that they would be paid
$75 for the successful completion of the research session and an
Table 1. Frequencies of Participants in the Experimental Conditions.
72
additional $100 if they were deemed by the interviewer as truthful
throughout the interview. This bonus was intended to motivate
the participants to convince the interviewer of his or her honesty.
In reality, all of the participants who successfully completed the
study were paid the full $175, regardless of the interviewer’s
determination.
This study was a 2 (deception) X 2 (concealment) X 2 (interview
style) factorial design. In the interview, participants were asked first
about their current residence, followed by their religious beliefs
and their employment. Participants were either instructed to tell
the truth about their current residence, but lie about their religious
beliefs and their employment status, or to tell the truth about all
three topics. The concealment aspect involved a final portion of the
interview and is not discussed in the results presented here.
Eligible participants were randomly assigned to one of eight
conditions. There were no significant differences in gender, race, or
age between these different groups, ps > 0.05. The frequencies of
participants in each experimental condition are presented in Table 1.
Participants were randomized to be interviewed in one of the two
styles described below.
Forcing
There are several sources currently available that provide
information about intelligence interviewing techniques, including
the U.S. Army Intelligence and Interrogation Handbook [23],
the Central Intelligence Agency’s KUBARK Counterintelligence
Interrogation Manual [24], and Gordon and Fleisher’s Effective
Interviewing & Interrogation Techniques [25]. Despite the breadth
of the Army handbook’s suggestions for educing information,
many sources note that the more coercive or confrontational
approaches contained in the handbook have often received
emphasis during training and have been overused in the field. In the
forcing interview, the interviewer tightly controls the course of the
conversation, and frequently challenges the participant’s motives
and responses through open skepticism or accusations of deceit.
The interviewer assesses the participant’s honesty, in part, on how
the participant reacts to these accusations. This style establishes
a comparatively adversarial relationship between the interviewer
and the participant.
Fostering
The fostering interview includes elements of motivational
interviewing and the cognitive interview. The cognitive interview
[26], [27] was originally developed to improve the ability of the
police to acquire the most accurate information possible from a
Forcing Fostering
Lie Condition Conceal No Conceal Total Lie Condition Conceal No Conceal Total
Lying 13 11 24 Lying 10 8 18
Truthful 8 8 16 Truthful 12 8 20
Total 21 19 40 Total 22 16 38
Detection of Deception in Structured Interviews Using Sensors and Algorithms

witness. The fostering style interview aims to establish a collaborative
relationship between the interviewer and the participant. In this
style, the interviewer adopts a friendlier demeanor. The interviewer
does not openly accuse the participant of lying, and his questions
never presume deceit. He asks open-ended questions designed to
elicit a wealth of reported information that the interviewer could
use to assess the participant’s honesty. The fostering interview
questions aim to establish a cooperative tone.
The following hypotheses generated for this study will be the focus
of this report:
1. Does the type of interview affect the interviewer’s ability to
detect deception?
2. How well do the physiologic sensors predict deception when
analyzed individually?
3. Does the type of interview influence the accuracy of physiologic
sensors in detecting deception?
Facilities Used for Experimentation
The facility used to conduct the experiments consisted of an
integrated, sensor-centric testing space consisting of a waiting area,
an assessment room, a noise-insulated testing room, an operations
room, and a data management room. The research staff executed
the research protocol in the waiting area and in the assessment
and testing rooms. These rooms were equipped with the different
physiological sensors that were used to collect participant data
during the execution of the research protocol. Temporal protocol
execution, sensor control, and data collection were remotely
controlled from the operations room. Finally, the collected
electronic sensor and experiment data were processed and stored
in the data management room.
Sensors Used for Data Collection
In the current study, we evaluated 14 features from 5 physiological
signals. Several nonverbal behavioral cues were assessed with a
Tobii eye tracker, including pupil size and blink rate. The LifeShirt
System (commercially available through VivoMetrics) measured
ECG and respiration. The electrodermal activity sensor from the
Lafayette polygraph was used to measure changes in the electrical
activity of the skin surface. These changes in electrical activity can
be thought of as indicators of imperceptible sweating that signify
sympathetic arousal. In addition, the plethysmograph from the
Lafayette polygraph was used. This photoelectric plethysmograph
measures rapidly occurring relative changes in pulse blood volume
in the finger.
Features of Interest
From the 5 signals collected, 14 features were analyzed as described
in Table 2. Some features have been reviewed sufficiently in the
literature that a direction of change can be hypothesized when
comparing feature values from baseline data to those gathered while
the subject was deceiving. In some cases, the direction of change is
not known with certainty. The features that consistently performed
better than chance included: interbeat interval, pulse area, pulse
width, peak-to-peak interval, and left and right pupil diameter. This
feature group comprises cardiac-related features as well as pupil
diameter features, and they will be discussed further here.
Detection of Deception in Structured Interviews Using Sensors and Algorithms
Interbeat interval was calculated by first decomposing the ECG
signal into peaks. A method for locating the R-peak (the highest
peak) in the ECG signal was adapted from an algorithm implemented
in the ECGTools package by Gari Clifford, which in turn was
inspired by the literature [28]. The highest and lowest points in
the difference signal were found via filtering and segmentation.
Each pair contained an R-peak, located at the time of the maximum
signal value in the interval. Once the peaks were located, the R-to-R
interval was calculated by taking the difference between times at
which successive peaks occurred.
In order to calculate the photoplethysmograph (PPG) features, it
was necessary to find the peaks and valleys that defined the signal.
This was done according to mixed state feature extraction derived
from an object tracking algorithm used to track fish movements in
video [29].
Pulse area was calculated as the sum PPG signal for one full cycle.
Pulse width was calculated at half of the maximum pulse height,
according to the full-width half-max (FWHM) norm. Peak-to-peak
interval was calculated as the difference between the times at
which two consecutive peaks occurred. Each peak must be part of a
complete cycle, which begins and ends with a valley.
The right and left pupil diameter features were read directly from
the data reported by the Tobii eye tracker.
Measures of Performance
Significance
The analysis invokes the following signal model for the measurement
of sensor data from multiple sensors to investigate whether
deception can be discerned through the observation of feature
values.
H : r = s + n
0 0
H : r = s + n
1 1
Here, H is assumed to be the hypothesis that the subject is
0
completely truthful and H is the hypothesis that the subject is
1
deceptive. The received signal, r, is a vector of features in RN , where
N is the number of features. The signal vectors for each hypothesis,
s and s , are the vectors of feature values generated under each
0 1
hypothesis and were assumed to possess Gaussian distributions,
although this assertion was not tested statistically. The additive
noise, n, was assumed to be all white Gaussian noise (AWGN) that is
identically distributed under each hypothesis.
Data for the H distribution were gathered from the interview on
0
residence, which was the first topic discussed in the interview.
These data were gathered either from the entire topical interview
or from immediate post-question periods. Data collected from the
entire topical interview were collected from several seconds prior to
the first question to 20 s after the last question, even though a brief
orienting response to the first question of the interview is expected.
Data collected from post-question intervals were collected from all
questions except the first question of the topical interview for 20
s beginning at the end of the question. Data for the H distribution
1
were gathered from the deception interview on employment/
religion in one of the two manners described above.
73

Table 2. Sensor, Description, and Anticipated Change Under Deception for Each Feature Calculated.
Feature Sensor Feature Description Expected Change Direction
Under Deception
Pupil Diameter (Right) EyeTracker Subject right eye pupil
diameter, in millimeters
Up
Pupil Diameter (Left) EyeTracker Subject left eye pupil diameter,
in millimeters
Blink Rate EyeTracker Subject eye blink frequency, in
hertz
The metrics used to describe significance were the t-test and effect
size. Two-tailed t-tests were used with an assumption of equal
variance and an alpha value of 0.05. Cohen’s d measure of effect size
was used. The general trend of a feature was assessed by looking at
the median effect size for that feature across a number of subjects.
74
d=
μ – μ0
1
(n 1 – 1) σ 2
1 + (n 0
n 1 + n 0 – 2
– 1) σ2
0
Detection
Test statistics were calculated as the z-score shown below, where t is
the test statistic, θ(x) is the test data point, μ 0 is the mean from the
H 0 distribution, and σ 0 is standard deviation of the H 0 distribution. In
this way, test statistics from individual subjects were appropriately
comparable.
t = θ(x) – μ 0
σ 0
These were used to create ROC curves that encapsulated the three
important quantities associated with any detection algorithm that
indicate how well it is able to detect deception over an ensemble
of subjects: Probability of Detection (PD), Probability of False
Alarm (PFA), and Area Under Curve (AUC). A z-score was used
to compare the AUC to 0.5, the AUC under a curve generated by
random guessing between classes [30]. A similar measure was used
to compare two ROC curves and to judge whether their difference
was statistically significant [31].
Results
Statistical Analyses
Does the type of interview affect the interviewer’s ability to detect
deception? Interviewer assessment accuracy was analyzed with
binomial tests to ascertain if accuracy was better than 50%.
Detection of Deception in Structured Interviews Using Sensors and Algorithms
Up
Down
Pulse Area Photoplethysmograph (PPG) Area of signal in one beat Down
Pulse Amplitude PPG Difference between peak
amplitude and trough amplitude
Down
Pulse Width PPG FWHM of peak Down
Peak-to-Peak Interval PPG Time between successive peaks Up
Electrodermal Activity (EDA)
Amplitude
EDA Finger Electrode Peak amplitude of skin resistance Up
EDA Duration EDA Finger Electrode Time for skin resistance to
return to preresponse level
EDA Line Length EDA Finger Electrode Length of skin resistance line
from peak to recovery
Interbeat Interval ECG (LifeShirt) Time between successive Rpeaks
of cardio signal
Respiratory Inhale/Exhale Ratio Inductive Plethysmograph
(LifeShirt Respiratory Sensor)
Ratio of the time interval from
one trough to the next peak to
the time interval of the peak to
the next trough
Respiratory Cycle Time LifeShirt Respiratory Sensor Time between successive peaks
in respiratory signal
Respiratory Amplitude LifeShirt Respiratory Sensor Difference between peak amplitude
and trough amplitude
Up
---
Up
Up
Up
Down

Analyses were performed separately for each interview type.
Seventy-seven participants were included in the analyses. Data
from one participant were not included because the person was
deemed ineligible. The results are shown in Table 3. Interviewers
were able to detect participant deception significantly better than
chance when interviewing in the fostering style (n = 38, accuracy
= 71%, p = 0.01), but not the forcing style (n = 39, accuracy = 62%,
p = 0.20).
Table 3. Interviewer Accuracy at Deception Detection.
Accuracy 62%
(24/39)
Forcing Fostering All
71%*
(27/38)
66%*
(51/77)
Interviews were conducted by two different interviewers, and
there were no significant differences in accuracy by interviewer,
p > 0.05. Interviewer 2’s accuracy in detecting lies about religion/
employment was significantly better than chance (accuracy = 70%,
p = 0.01 ). There was no significant difference in detecting deception
between interviewers on the basis of interview style.
Participant anxiety was assessed for changes due to the interview.
Participants were significantly more anxious during the interview
(M = 35.69, SD = 11.22) than they were before (M = 30.22, SD =
8.73) (t(77) = -4.63, p < 0.05). There were no significant differences
in anxiety change scores by interview type (fostering M = -4.13, SD
= 11.44; forcing M = -6.75, SD = 9.38), or lie condition (deceptive M
= -6.83, SD = 11.43; honest M = -3.89, SD = 9.07).
Feature Analyses
Significance Testing
How well do the physiologic sensors predict deception when analyzed
individually? Test statistics generated using residence interview
post-question interval data for background and the mean of the
employment/religion interview post-question interval data were
correlated with dichotomous criteria:
• Interview Type (forcing coded 0, fostering coded 1).
• Deception State (deceptive coded 0, truthful coded 1).
The results can be seen in Table 4. Interbeat interval, peak-to-peak
interval, pulse area, and pulse width were significantly correlated
with deception state; this is indicated by the bold type in the table.
Features that did not have significant correlations are not listed
in the table. Pulse amplitude showed a positive correlation with
interview type but not with deception state.
Detector ROC Curves
Deception detectors were built from distributions garnered from
each interval option for each feature. The AUC for detectors that
performed significantly better than chance at the α = 0.05 level are
reported in Table 5 along with the sample size for the given ROC
curve. LifeShirt data were not recoverable for 2 out of 77 subjects,
lowering the sample size for features from the LifeShirt sensors.
Further, one subject had poor quality ECG data during a portion of
Detection of Deception in Structured Interviews Using Sensors and Algorithms
Table 4. Deception Post-Question Test Statistic Correlations with
Interview Type and Participant Deception State. Positive correlation
indicates that participants in the given state have smaller test
statistics. *p < 0.05 **p < 0.01
Feature Interview Type Deception State
Interbeat Interval, ECG 0.289*
Peak-to-Peak Interval,
PPG
Pulse Amplitude 0.233*
0.243*
Pulse Area 0.324**
Pulse Width 0.225*
Table 5. Significant Deception Detector Results for Each of 8 Features
Measured with Both Intervals. Detectors are statistically different
from chance at the α = 0.05 level. nonsignificant detectors are not
shown. All test statistics were generated with a mean test value.
Feature Post-Question Entire Topical
Interview
Interbeat Interval, ECG 0.703 (N = 75) 0.794 (N = 74)
Right Pupil Diameter 0.720 (N=77)
Left Pupil Diameter 0.691 (N = 77)
Respiratory Inhale/Exhale
(I/E) Ratio
0.647 (N = 75)
Respiratory Cycle Time 0.304 (N = 75)
Pulse Area 0.693 (N = 77) 0.778 (N = 77)
Pulse Width 0.663 (N = 77) 0.697 (N = 77)
Peak-to-Peak Interval,
PPG
0.673 (N = 77) 0.762 (N = 77)
one of the interviews; this prevented analysis of the interview as a
whole without disrupting the post-question analysis for the interbeat
interval feature. In all cases where a ROC curve was significant for
both entire topical interview intervals and post-question intervals,
the AUC for the entire topical interview-generated ROC curve was
higher. The two best performing detectors for both interval options
were from the interbeat interval and peak-to-peak interval features.
For the entire topical interview data interval, these both produced
curves with AUC higher than 0.75. For both interval options, both of
these curves performed significantly better than chance, but they
were not significantly different from each other.
Does the type of interview influence the accuracy of physiologic sensors
in detecting deception? Correlations between deception post-question
interval test statistics and deception state were computed. Three
features had positive correlations with deception state under the forcing
interview style. These were interbeat interval (0.355, p < 0.05), pulse
75

area (0.401, p < 0.05), and peak-to-peak interval (0.410, p < 0.01). Only
under the forcing interview style did feature values from the deception
interview on employment/religion correlate with the deception state.
Deception detectors were generated for all features using data
from the entire topical interview. The detectors for all features are
compared for the two different interview styles via their area under
curve in Figure 1. The maximum area under curve possible is one,
and a detector that performs at chance will have an area of 0.5. The
pulse-based features (interbeat interval, pulse width, peak-to-peak
interval, and pulse area) as well as the pupil diameter features in the
forcing interview style performed the best, all with an area above
0.7. All of these ROC curves were significantly better than chance. In
the fostering interview style, only the pulse area feature performed
significantly better than chance.
Forcing
Fostering
76
Area Under the Curve for Detector Families
interBeatInterval
rightPupilDiameter
leftPupilDiameter
eyeBlink
edaAmplitude
edaDuration
edaLineLengths
respiratoryAmplitude
respiratoryieRatio
respiratoryCycleTime
pulseArea
pulseAmplitude
pulseWidth
peakToPeakInterval
Table 6. Forcing Interview Style Detector Results Summary.
Detectors were generated for both conditions and both interval
types. For each combination, the features producing significant
detectors are reported with the AUC. All detectors were built with
test statistics garnered from mean test values.
Condition Interval Features Producing
Significant Detectors
Deception Post-question Interbeat Interval, ECG (0.730)
Respiratory I/E Ratio (0.790)
Pulse Area (0.775)
Pulse Width (0.775)
Peak-to-Peak Interval (0.785)
Deception Entire topical
interview
Detection of Deception in Structured Interviews Using Sensors and Algorithms
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Figure 1. AUC for ROC curves generated from entire topical interview
intervals for deception detection, comparing results for forcing and
fostering interview styles.
Significant detectors for the forcing interview style are summarized
in Table 6. Detectors that performed significantly better than
chance are listed along with their AUC for each condition, interval
type, and feature. The detector with the highest area is the interbeat
interval deception detector that operates on the entire topical
interview distributions. It has an area of 0.863. For both interval
types, interbeat interval, pulse area, pulse width, and peak-to-peak
interval make significant deception detectors.
Only the pulse area feature yielded a significant detector (AUC
0.649) for the fostering interview style. This was the case when
entire topical interview intervals were used for deception detection.
The best performing feature from all combinations of interval
choice and interview style was interbeat interval. The deception
Probability Detection
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
Interbeat Interval, ECG (0.863)
Right Pupil Diameter (0.720)
Respiratory Cycle Time (0.250)
Pulse Area (0.846)
Pulse Width (0.751)
Peak-to-Peak Interval (0.855)
detection performance of this feature was enhanced when data
only from the forcing style interview were used. In Figure 2, the
entire topical interview interval detector for interbeat interval
from forcing interview participants is shown in comparison to the
comparable detector from the fostering interview participants.
The forcing interview style detector performed significantly better
than chance. (The detector from the fostering participants was not
statistically significantly better than chance.)
ROC Curves
InterBeatInterval - Forcing (0.863)
InterBeatInterval - Fostering (0.658)
0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Probability False Alarm
Figure 2. ROC curves comparing interview styles for entire topical
interview intervals data for interbeat interval, ECG. Fostering detector
was not significantly better than chance.

Discussion
We have demonstrated the promise of certain sensor signals,
features, and their analysis parameters in detecting lies. Further,
we have demonstrated the effects of interview style on the
detection of deception in an interview recorded along with
physiological signals.
Sensor signal analysis results included higher successful
classification and more significant results among participants who
underwent the forcing interview style than the fostering interview
style. This was apparent through several different analysis
approaches. While five features produced significant detectors for
the forcing interview style subset, there was only one feature with
a significant detector for the fostering subset. The highest area
under the curve was 0.863, produced by the interbeat interval
feature calculated from the ECG signal of forcing interview
participants. When deception test statistics were correlated with
deception state for participants who underwent each interview
style, significant correlations only occurred in the subset that
underwent the forcing interview style.
The features that consistently performed better than chance
included: interbeat interval, pulse area, pulse width, peak-to-peak
interval, left pupil diameter, and right pupil diameter. This feature
group comprises cardiac-related features measured from both the
ECG and PPG signals as well as pupil diameter features. Significant
detectors were also produced by the respiratory I/E ratio and
respiratory cycle time features. These will not be discussed as they
were not significant in the correlation analysis, nor did they show
a moderate or large effect size. The cardiac and pupil features will
be discussed further here.
A number of cardiac features showed significant correlations with
deception state, had moderate-to-high effect size differences, or
generated significant detectors. Interbeat interval and peak-topeak
interval decreases were significant as measured by effect
size on both short (20 s) and long (entire 5-min interview) time
scales. These features were also significantly correlated with
deception state and they produced detectors with the highest
area for both data interval definitions. Although the differences
between the two features were not significant, interbeat interval
had consistently higher correlations, effect sizes, and areas under
curve. The strong results shown by the interbeat interval and
peak-to-peak interval features are indicators that deception can
be measured effectively by an increase in heart rate. This is in
contrast to previous studies that have found a heart rate decrease
when participants are deceptive.
There has been some debate regarding the heart rate (HR)
response to deception. Some have found HR responses to
deception to be biphasic [8], [32]. There is an initial increase in
HR for the first 4 s following question onset, followed by a decrease
until approximately the 7th post stimulus second, and the HR
then returns to baseline. Others have found HR deceleration to be
indicators of deception [33], [34]. There also has been discussion
in the literature as to the nature of the HR response to different
types of deception tests [5], [32], [10]. The authors have noted
that the direct and often accusatory questions that comprise
Detection of Deception in Structured Interviews Using Sensors and Algorithms
the comparison question test may produce defensive responses,
evidenced in part by HR acceleration, whereas the stimuli used
in a guilty knowledge test may produce orienting responses,
evidenced in part by HR deceleration. The HR increase found
in the present study could be indicative of a defensive response
since the test format is similar to that of the comparison question
test. A change in time window does not explain the discrepancy
between the HR decrease reported in the literature and the HR
increase measured in this study. The responses recorded here also
do not follow the biphasic theory as a HR increase was observed
both in the 4-s window immediately after question onset as well as
in the window from 6 to 12 s after question onset.
Other cardiac measures also showed promise. Both pulse area and
pulse width had significant correlations with deception state as
well as significant detectors. Pulse amplitude, however, was a less
promising feature. The pulse amplitude feature is normalized with
respect to baseline signal value, while the pulse area feature was
not implemented in this way. This may be why pulse amplitude
was not a significant predictor of deception while pulse area was.
A decreasing DC signal component could cause an erroneously
significant result for the pulse area feature, and this possibility
should be avoided in future implementations by eliminating the
DC component of the signal or by subtracting a baseline or valley
value from each measure of pulse area.
Pupil diameter measures in the left and right eye did not show
significant correlations with deception state, but they exhibited
moderate mean effect size differences, and on the entire topical
interview interval, they generated detectors that performed better
than chance. Detectors generated for entire topical interview
background intervals and tested with data from post-question
intervals were also not significant. Significance on entire topical
interview interval detectors may indicate that participants’ pupil
diameter was more likely to increase in response to a question
that required an answer more detailed than ‘yes’ or ‘no.’ The postquestion
interval data distributions are drawn from select yes/no
questions in the experiment.
Two data intervals were considered here, and there were several
instances in which the entire topical interview data intervals
were more informative than the 20-s post-question intervals.
(For an example, see Table 5.) The benefits of using entire topical
interview intervals for data collection were not observed when
the background distribution was gathered from the entire topical
interview if the test data came only from post-question intervals.
This suggests that because there is more dialogue involved in
an interview as compared with a more traditional detection of
deception test in which each question is answered with a simple
yes/no, there may be more physiological activity and more
information that can be extracted from an entire topical interview
as opposed to a 20-s post-question interval. This is the case even
when those questions are quite to the point of the matter at hand
(e.g., “Are you being truthful when you tell me that you work as a
retail salesperson?”)
Study design may have also impacted the utility of these data
intervals. Although the wording of the questions asked during the
77

interviews was similar to those asked in a traditional deception
detection test, the test structure was different. In one type of
deception detection test, the comparison question test, each
relevant question is preceded by a comparison question, and
decisions regarding veracity are made by comparing responses to
the two question types [35]. In the present study, the questions
used for comparison were asked early in the interview. The
deception questions were presented toward the middle of the
interview. It may be difficult to see a change in post-question
autonomic responding between questions that are presented far
apart during the interview.
Conclusions and Future Work
Interview style impacts interviewer assessment accuracy.
Interviewer accuracy at detecting deception was better than
chance in the fostering interview style. Rapport developed
during a fostering interview may facilitate the interviewer’s
ability to detect deceit. In the forcing interview style, interviewer
assessment accuracy was not statistically different from chance.
A forcing style interview amplifies physiological signals indicative
of deception. When the forcing interview style was used, sensor
signals yielded detectors that operated significantly better than
chance. This showed an advantage over interviewer assessment
accuracy that was not better than chance.
Physiologic information elicited during topical interviews may
be more indicative of deception than physiologic information
gathered from periods of structured yes/no questions, although
the sources for physiologic changes may be more difficult to
identify. This trade-off should be a topic of further study.
Heart rate and other pulse-based features show good capability
in deception detection. Our results indicate a need for better
understanding of the orienting and defensive responses and when
to expect each.
With regard to pupil diameter, our results are suggestive, but not
as strong as the evidence that others have shown.
Interview-based deception detection techniques should be
pursued further in cases where deception detection with
physiological sensors is desired. Placement of comparison
questions should be reevaluated.
Draper Laboratory continues to expand its facilities, resources,
and expertise to pursue important challenges in this area,
including unstructured interview analysis, remote sensing of
physiology, and contextual factors in educing information.
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79

80
Meredith G. Cunha is a Member of the Technical Staff in the Fusion, Exploitation, and Inference Technologies
Group. She has experience with data analysis and pattern classification of hyperspectral, biochemical sensors and
physiological data. Her recent work is in physiological and psychophysiological signal processing. Mrs. Cunha
received the Bachelor of Science and Master of Engineering degrees from the Electrical Engineering and Computer
Science Department at MIT.
Alissa C. Clarke is a research consultant at MRAC LLC. She has worked in collaboration with Draper Laboratory on
several studies in the area of deception detection, supporting the development of research protocols, coordinating
the recruitment and testing of participants, and aiding in the preparation of final reports. Ms. Clarke received an
A.B. in Psychology and Health Policy from Harvard University.
Jennifer Z. Martin is an Advisor and Senior Research Scientist with MRAC. She is currently involved with several
research projects, including work on intelligence interviewing and cues to deception or malintent (the intent
or plan to cause harm). She is an author of the Theory of Malintent, which drives the Department of Homeland
Security (DHS) Future Attribute Screening Technologies (FAST) program, and helped devise the malintent research
paradigm. Prior to her work with MRAC, she excelled in both corporate and academic settings. Dr. Martin received
a Ph.D. in Experimental Social Psychology from Ohio University.
Jason R. Beauregard is a Research Associate with MRAC. Since joining the firm, he has managed several research
projects spanning a variety of topics and utilizing unique protocols. His responsibilities include supervising protocol
planning, development, implementation, and operation of human subject testing. Prior to his employment with
MRAC, he served as a Case Manager, Intervention Specialist, and Assistant Program Director of a Court Support
Services Division (CSSD)-sponsored diversionary program in the state of Connecticut. Mr. Beauregard received a
B.A. in Psychology from the University of Connecticut.
Andrea K. Webb is a Psychophysiologist at Draper Laboratory. She has an extensive background in
psychophysiology, eye-tracking, deception detection, quantitative methods, and experimental design. Her work at
Draper has focused on security screening, interviewing, autonomic specificity, and post-traumatic stress disorder
(PTSD). She is currently Principal Investigator for a study examining autonomic responses in people with PTSD
and is the data analysis lead for a project funded by DHS. Dr. Webb earned a B.S. in Psychology from Boise State
University and M.S. and Ph.D. degrees in Educational Psychology from the University of Utah.
Detection of Deception in Structured Interviews Using Sensors and Algorithms

Asher A. Hensley is a Radar Systems Engineer at the Telephonics Corporation. His background includes work in
sea clutter modeling, detection, antenna blockage processing, and tracking. His primary research interests are in
machine learning and computer vision. Mr. Hensley received a B.Sc. in Electrical Engineering from Northeastern
University and is currently pursuing a Ph.D. in Electrical Engineering from SUNY Stony Brook.
Nirmal Keshava is the Group Leader for the Fusion, Exploitation, and Inference Technologies group at Draper
Laboratory. His interests include the development of statistical signal processing techniques for the analysis of
physiological and neuroimaging measurements, as well as the fusion of heterogeneous data in decision algorithms.
He received a B.S. in Electrical Engineering from UCLA, an M.S. in Electrical and Computer Engineering from Purdue
University, and a Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University.
Daniel J. Martin, Ph.D., ABPP, is the Director of MRAC LLC, a research and consulting firm that specializes in bridging
the gap between empirical knowledge and corporate or government applications. He is also the Director of Research
for the DHS’s FAST program and serves as experimental lead on several research studies investigating security
screening and interviewing. His research interests include the effectiveness of different interviewing methodologies
in eliciting information and the psychological and physiological cues to deception and malintent. Dr. Martin joined the
faculty at Yale University in 1999. There he conducted several multisite clinical trials in substance abuse treatment.
He also has over 10 years of experience training hundreds of individuals in motivational interviewing with resistant
populations. Dr. Martin is board certified in Clinical Psychology by the American Board of Professional Psychology.
Detection of Deception in Structured Interviews Using Sensors and Algorithms
81

Planner Complexity
82
Planner Complexity with Operator Interaction
Human
1/Mission
Urban Challenge
Increasing
Environment
Uncertainty
Aircraft
Autopilot
Active Military
Robots
Teleoperated
Remotely operated robotic systems have demonstrated life-saving
utility during U.S. military operations, but the Department of Defense
(DoD) has also seen the limitations of ground and aerial robotic systems
that require many people for operations and maintenance. Over time,
the DoD envisions more capable robotic systems that autonomously
execute complex missions with far less human interaction. To enable
this transition, the DoD needs to clearly understand the trade-offs that
must be made when choosing to develop an autonomous system. There
are many circumstances where actions that are straightforward for a
manned system to accomplish are enormously difficult — and therefore
costly — for machine systems to handle.
This developmental paper addresses the need to define understandable
requirements for performance and the implications of those requirements
on the system design. Instead of attempting to specify a “level” of
“autonomy” or overall “intelligence,” the authors propose a starting
set of quantifiable — and testable — requirements that can be applied
to any autonomous robotic system. These range from the dynamics
of the operating environment to the overall expected assertiveness of
the system when faced with uncertain conditions. we believe a solid
understanding of these expectations will not only benefit the system
development, but be a key component of building trust between humans
and robotic systems.
Requirements-Driven Autonomous System Test Design: Building Trusting Relationships

Requirements-Driven Autonomous System Test
Design: Building Trusting Relationships
Troy B. Jones and Mitch G. Leammukda
Copyright © 2010 by the Instrumentation Test and Evaluation Association (ITEA). Presented at the 15th Annual Live-Virtual-
Constructive Conference, El Paso, TX, January 11 - 14, 2010. Sponsored by: ITEA
Abstract
Formal testing of autonomous systems is an evolving practice. For these systems to transition from operating in restricted (or completely
isolated) environments to truly collaborative operations alongside humans, new test methods and metrics are required to build trust
between the operators and their new partners. There are no current general standards of performance and safety testing for autonomous
systems. However, we propose that there are several critical system-level requirements to consider for an autonomous system that can
efficiently direct the test design to focus on potential system weaknesses: environment uncertainty, frequency of operator interaction, and
level of assertiveness. We believe that by understanding the effects of these system requirements, test engineers–and systems engineers–will
be better poised to develop validation and verification plans that expose unexpected system behaviors early, ensure a quantifiable level
of safety, and ultimately build trust with collaborating humans. To relate these concepts to physical systems, examples will be related to
experiences from the Defense Advanced Research Projects Agency (DARPA) Urban Challenge autonomous vehicle race project in 2007 and
other relevant systems.
Introduction
The adoption of autonomous systems in well-defined and/or
controlled operational environments is common; commercial and
military aircraft routinely rely on advanced autopilot systems for
the majority of flight duties, manufacturing operations around
the world employ vast robotic systems, and even individuals rely
on increasingly “active” safety systems in automobiles to reduce
injuries from collisions.
On the surface, based on these trends, adding levels of autonomy in
any of these existing systems and deploying new even more helpful
systems seems not only inevitable but a straightforward extension of
existing development, testing, and deployment methods. However,
until fundamental changes in social, legal, and engineering practice
are made, the amazing autonomous system advances being
demonstrated at universities and research laboratories will remain
educational experiments. We see at least three challenges:
1. People must trust an autonomous system in situations when
it may harm them: Arguably, people already trust complex
autonomous systems under circumstances such as the aircraft
autopilot, but passengers know that a human is supervising that
system constantly.
2. Legal ramifications of injuries or deaths resulting from the
actions of autonomous systems must be clearly defined:
When an autonomous system causes a death (which certainly
will happen), what party is held liable for that injury or death?
3. There must be well-defined standards to test the autono
mous system to operate in the required environments with
Requirements-Driven Autonomous System Test Design: Building Trusting Relationships
“acceptable” performance: Defining what is or is not “acceptable”
performance for an autonomous system ties directly into how well
people will ultimately trust that system and will ease the definition
of fair legal responsibilities.
In this paper, we examine perhaps the easiest of these topics:
proposed methods for specifying and ultimately testing the
performance of autonomous systems. As engineers, we are
responsible for supplying the supporting evidence to the
customer that new autonomous systems will meet expectations of
performance and safety.
Draper Laboratory has worked in autonomous system evaluation
for many years [1]. This paper describes a new approach for
autonomous system requirements development and test design
based largely on experiences gained during our participation in the
DARPA Urban Challenge autonomous vehicle race held in 2007.
These concepts are still in development and will be refined as we
evaluate more systems and collaborate with other members of the
engineering community.
Autonomous System Characteristics
There are as many definitions of “autonomous systems” as there
are papers that define it. Instead of creating yet another incomplete
definition, we propose that there are common sets of traits
that can be specified for any automated/autonomous/robotic/
intelligent system. These traits help establish what performance is
expected of the system (thereby providing a basis for system-level
requirements), and effectively point out the most critical areas for
test and evaluation.
83

These characteristics are intentionally structured in easily
comprehended terms with the goal of improving how operators and
observers understand the actions of an autonomous system. The
following sections will explain these characteristics and include
examples of how they drive the autonomous system requirements
and testing. Unfortunately, these characteristics are highly coupled
and not necessarily in a linear fashion, but understanding these
interrelationships is a key area of ongoing work.
Environment Uncertainty
We live in an uncertain world: Our perceptual abilities (visual,
auditory, olfactory, touch) are constantly (and unconsciously) at
work keeping us informed about changes in our environment. When
designing an autonomous system to function in this uncertain
world, we need to carefully understand the environment in which
we expect the system to operate. Furthermore, we propose that
classifying environmental uncertainty is performed adequately
by answering the question: “What is the reaction time we expect
from the system to detect and avoid collisions with objects in the
environment?”
Above all other characteristics, environmental uncertainty is the
primary driver for how much perceptual ability an autonomous
system requires to do its job. How well does the system need to
“see” the environment in order to react to potential hazards and
accomplish its mission?
This discussion of environment uncertainty is restricted to visual
types of perception, but we believe autonomous systems will need
to take advantage of other “senses” to eventually meet our (human)
expectations of performance.
Perception Coverage
We define the perception coverage as the percentage of spherical
volume around a system that is pierced by a perceptual sensing
system. For an easy-to-understand example, we begin by estimating
the perception coverage for a human visual system.
Human Visual Perception Coverage
Since we desire a nondimensional metric, we will choose an arbitrary
radius, in this case 100 m, for the spherical volume and project
how much of the volume is seen by human eyes. This graphical
construction is shown in Figure 1 and shows that human vision
in a given instant of time can perceive about 40% of the volume
around your head. Of course, we can rapidly scan our environment
by rotating our heads and bodies, thus providing a complete visual
scan in seconds.
What does this mean with regard to environmental uncertainty?
Certainly humans are very adept at operating in highly uncertain
conditions and do so with a high degree of success. Therefore, we
propose that the human instantaneous perceptual coverage (visual
in this case) is an intuitive upper bound on the same metric for an
autonomous system.
Having this large amount of input perceptual information at all
times gives us excellent awareness of changes in our environment.
It has been shown [2] that humans see, recognize, and react to a
visual stimulus within 400-600 ms of seeing the stimulus. This range
then is a practical lower limit on how quickly we should expect an
autonomous system to react to changes in the environment.
84
Figure 1. Human visual perceptual coverage, approximately = 40%..
Autonomous System Perception Coverage
We now select an example of an autonomous system that operates
in a high uncertainty environment, the MIT Urban Challenge LR3
autonomous vehicle, Talos, shown in Figure 2, and estimate the same
metric. This system completed approximately 60 mi of completely
autonomous driving in a low-speed race with human-driven and
other autonomous vehicle traffic. To do this, Talos has a myriad of
perceptual sensor inputs [3]:
• 1 x Velodyne HDL-64 LIDAR 360-deg 3D scanning LIDAR.
• 12 x SICK planar scanning LIDAR.
• 5 x Point Grey Firefly MV cameras.
• 15 x Delphi automotive cruise control radars.
ACC RADAR (15)
Skirt
SICK LIDAR (7)
Figure 2. MIT Urban Challenge vehicle Talos.
Requirements-Driven Autonomous System Test Design: Building Trusting Relationships
Velodyne HDL
Pushbroom
SICK LIDAR (5)
Cameras (6)
The most useful of these perceptual inputs for detecting obstacles
and vehicle tracking [3] is the Velodyne HDL-64. It contains an array
of 64 lasers mounted in a head unit that covers a 26-deg vertical

ange [4]. Motors spin the entire head assembly at 15 revolutions
per second, generating approximately 66,000 range samples per
revolution, or about 1 million samples per second of operation.
Each full revolution of the Velodyne returned a complete 3D point
cloud of distances to objects all around the vehicle and it was by far
the most popular single sensor to have in the Urban Challenge (if
your team could afford it).
A single sensor that returns continuous data around the entire
vehicle eliminates the need to construct a 3D environment model
out of successive line scans from multiple planar LIDAR (such as
the SICK units), which is a computationally intensive and errorprone
process.
Performing the same calculation of perception coverage for a
Velodyne HDL-64 LIDAR involves representing the geometry of
the sensing beams. For the human vision system, we assumed the
resolution of the image data is practically infinite, but the LIDAR is
restricted to 64 discrete beams of distance measurement that are
swept around a center axis. To perform the calculation, we assumed
that each beam has a nominal diameter of 1/8 in, does not diffract,
and assumed that each revolution of a beam was a continuous disk
of range data, when in fact each revolution is a series of laser pulses.
Based on those (generous) input assumptions, we created a
graphical construction of perception coverage for the Velodyne,
which is shown in Figure 3. We discovered that a single scan is
approximately 0.1% coverage, that is, 400 times less than a single
instant of human visual information. Despite the large disparity
with human ability, the Velodyne proved to be an adequate primary
sensor in the context of the Urban Challenge.
Talos had several methods to detect and avoid collisions with
objects that reduced its effective reaction time [3]. However, for this
example, we limit the reaction time estimate based on the rules of
the DARPA Urban Challenge, which placed a 30-mph speed limit on
all vehicles [5]. If we consider the case of two vehicles in opposing
lanes of travel, we have a maximum closing speed of 60 mph (27
m/s). Talos used approximately 60 m of the Velodyne’s 100+ m
range [4] for perception, and therefore would have just over 2 s in
which to react to an oncoming vehicle in the wrong lane.
Clearly, there is a relationship between the operating environment
of a system and the perceptual capabilities needed to operate in that
environment, and we illustrate this by using the two examples given
and the addition of a third point: We assume that in order to react
to uncertainty instantly, a system would need 100% perceptual
coverage (and the ability to process and decide actions instantly).
These data points and a qualitative relationship between them are
shown in Figure 4.
It is logical that decreasing the uncertainty in the environment
should reduce the need for perception, and the same is true for the
converse.
While not the final answer to how to specify requirements for an
autonomous system perception system, we believe it is a start that
leads to metrics for testing the perception coverage of the system.
In fact, it is very compelling that vehicles in the Urban Challenge
were able to safely complete the race with so little perceptual
information overall.
Requirements-Driven Autonomous System Test Design: Building Trusting Relationships
Figure 3. Perception coverage for Velodyne HDL-64 LIDAR
system, ~0.1%.
80
60
40
20
Perception Coverage (%) 100
Perception Coverage with Environmental Uncertainty
Human
Reaction Time
Human
Visual
Blind
Visible
Urban Challenge
Required Reaction Time
Velodyne
HDL 360-deg
LIDAR
0
0 0.5 1 1.5 2
Potential Time to Collision (s)
Figure 4. Variation in perception coverage as driven by the
environment uncertainty.
Environmental Uncertainty Test Concepts
Clear requirements on perception coverage and potential time-tocollision
will bound the test design for environmental stimuli. It is
up to the test engineering team to design experiments that validate
the ability of the system to meet performance goals and also stress
the system to find potential weaknesses.
If a system is designed to operate in a low uncertainty environment
all the time (e.g., on factory floor welding metal components), the
perception-related tests required are limited to proper detection of
85

the work piece and the welded connections. If an operator enters
a work zone of the system as defined by a rigid barrier, the system
must shut down immediately [6].
On the other extreme, an autonomous system operating in a dynamic
environment, be it on the ground or in the air, needs the perceptual
systems stressed early and as often as possible. As we discovered
during the DARPA Urban Challenge experience, changing the
operating environment of the system always revealed flaws in the
assumptions made during the development of various algorithms.
Perception systems should be tested thoroughly against many kinds
of surfaces moving at speeds appropriate to the environment, as
both surface properties and velocity will impact the accuracy of the
detection and classification of objects [3]. In addition, test cases for
tracking small objects, if applicable, can be very challenging due to
gaps in coverage and latency of the measurements.
Frequency of Operator Interaction
When developing any system, it is critical to understand how the
users interact with it. This information can be captured in “Concept
of Operations” documents that specify when users are expecting to
input information into or get information out of a system. This same
concept must be applied to autonomous systems with a slight shift
in implications.
When we are developing an autonomous system, we need to
establish expectations on how much help a human operator is
expected to provide during normal operations. Ideally, an entirely
autonomous system would require a single mission statement and
it would execute that mission without further assistance. However,
just as people sometimes need additional inputs during a task, an
autonomous system requires the same consideration.
On the other end of the spectrum, an autonomous system can
degenerate into an entirely remotely-controlled system. The human
operator is constantly in contact with the system, providing direct
commands to accomplish the task.
In this section, we explore the impact of specifying the required
level of operator interaction. This characteristic in particular has
far-reaching implications, and unlike environmental uncertainty,
is fully controlled by the customer and developer of the system.
A customer can choose (for example) to require an autonomous
system to need only a single operator interaction per year, but that
requirement will significantly impact development time and cost.
Planner Complexity
If the autonomous system is intended to operate with very little
operator interaction, then that system must be able to effectively
decide what to do on its own as the environment and mission evolve.
We will refer to this capability generically as “planning” rather
than “intelligence.” The planner operation is central to how well
autonomous systems operate in uncertain environments. We will
review some examples of planning complexity and how it relates
to operator inputs. Additionally, when ranking complexity, we need
to consider the operating environment of the system. A planning
system that operates in a highly uncertain environment must adapt
quickly to changes in that environment, whereas low uncertainty
environments can be traversed with perhaps only a single plan for
the entire mission.
86
Aircraft Autopilot
Everyday autopilot systems in commercial and military aircraft
perform certain planning tasks based on pilot commands. Modern
autopilot systems have many potential “modes” of operation, such
as maintaining altitude and heading or steering the aircraft to follow
a set course of waypoints [7]. Even though the pilot must initiate
these modes, once activated, the autopilot program can make
course changes to follow the desired route and therefore is planning
vehicle motion. However, an aircraft autopilot program will not
change the course of the aircraft to avoid a collision with another
aircraft [8]. Instead, the pilot is issued an “advisory” to change
altitude and/or heading.
With this basic understanding of what an autopilot is allowed to
do, we rank the planner complexity of these systems as low. Since
most aircraft with autopilot systems operate in air traffic controlled
airspace, we also believe the environmental uncertainty is low,
placing the autopilot planner complexity on a qualitative graph as
shown in Figure 5.
Planner Complexity with Operator Interaction
Frequency of Operator Interaction
Figure 5. Variation in planner complexity as function of required
frequency of operator interaction.
Requirements-Driven Autonomous System Test Design: Building Trusting Relationships
Planner Complexity
Human
1/Mission
Urban Challenge
Increasing
Environment
Uncertainty
Aircraft
Autopilot
Active Military
Robots
Teleoperated
Urban Challenge Autonomous System
Vehicles that competed in the Urban Challenge were asked to
achieve a difficult set of goals with a single operator interaction
with the system per mission. After initial setup, the vehicle was
required to negotiate an uncertain environment of human-driven
and autonomous traffic vehicles without assistance.
Accomplishing this performance implied a key top-level design
requirement for the planning system: it must be capable of
generating entirely new vehicle motion plans and executing them
automatically. For example, both the 4th place finisher, MIT, and
the 1st place finisher, Carnegie Mellon (Boss), relied on planning
systems that were constantly creating new motion plans based on
the perceived environment [9], [10]. In the case of Talos, the vehicle
motion planning system was always searching for safe vehicle plans

that would achieve the goal of the next mission waypoint, but also
possible plans for bringing the vehicle to safe stop at all times. This
strategy was flexible and allowed the vehicle to execute sudden
stops if a collision was anticipated.
This type of flexibility, however, comes at a high complexity cost, at
least when compared with traditional systems that are not allowed
to replan their actions automatically without human consent. The
motion plans were generated continuously at a 10-Hz rate and
could represent plans up to several seconds into the future [9].
The dynamic nature of the planning was founded on incorporating
randomness in the system, meaning that there was no predefined
finite set of paths from which the system was selecting. Instead, it
was constantly creating new possible plans and selecting them
based on the environmental and rule constraints.
We feel this adapting type of planning system is the evolutionary
path to greater autonomous vehicle capability and it represents
a high level of complexity. But the Urban Challenge systems still
operated in a controlled environment with moderate levels of
uncertainty, so we rank the planner complexity well above the
autopilot case and on a higher environment uncertainty curve.
Human Planning
For the upper bound of the relationship, we rank human planning
processes as extremely adaptable and highly complex, giving
the highest level complexity ranking for the most uncertain
environments, and likely off the notional planner complexity scale
as shown.
Remotely Operated Systems
The lowest end of the planning complexity curve is occupied by
remotely operated systems. These systems depend on a human
operator to make all planning decisions. For this ranking, we consider
only the capabilities of the base system without the operator. We
understand that indeed a great advantage of remotely operated
systems is the planning capability of the human operator. Currently,
most active robots used by the military fall into this classification.
Verification Effort
The frequency of interaction with an autonomous system is a
powerful parameter stating how independent we expect the system
to be over time (and is also tightly related to the level of assertiveness
in the next section). Intuitively, we expect that the more independent
a system is, the more time must be spent performing testing to verify
system performance and safety. The following examples will help
create another qualitative relationship between verification effort
and the required frequency of operator interaction.
Aircraft Autopilot
As an example, we first consider the very formal verification process
performed for certification of aircraft autopilot software (and other
avionics components), as recommended by the Federal Aviation
Agency (FAA) [11]. Autopilots are robust autonomous systems
flying millions of miles without incident [12]. Organizations are
required to meet the guidelines set forth in DO-178B [13] (and the
many ancillary documents) in order to achieve autopilot software
certification. The intent of these standards is to provide a rigorous
set of processes and tests that ensure the safety of software products
that operate the airplane. The process of achieving compliance
Requirements-Driven Autonomous System Test Design: Building Trusting Relationships
with DO-178B and obtaining the certification for new software is
so involved that entire companies exist to assist with/perform the
process or create software tools to help generate software that is
compliant with the standards [14]-[16]. Therefore, we classify
aircraft avionics software as being a “very high” level of verification
effort, not the highest, but certainly close. And remember, we
classified the complexity of the planning software as low.
For this example, we will quantify an aircraft autopilot as needing
inputs from the human operators from several to many times in a
given flight. The pilots are responsible for setting the operational
mode of the autopilot and are required to initiate complex
sequences like autolanding [7]. Therefore, we place aircraft
autopilot on a verification effort versus frequency of operator
interaction as shown in Figure 6.
Verification Effort
Verification and Comms with Operator Interaction
1/Mission
Aircraft Avionics*
Urban Challenge
Verification Effort
Bandwidth
Active Military
Robots
Bandwidth of Comm Link
Teleoperated
Frequency of Operator Interaction
Figure 6. Verification effort and communications bandwidth as a
function of operator interaction.
Urban Challenge Autonomous System
DARPA required all entrants in the Urban Challenge to have only a
single operator interaction per mission in order to compete in the
race. The operators could stage the vehicle at the starting zone,
enter the mission map, arm the vehicle for autonomous driving, and
walk away. At that point, the operators were intended to have no
further contact, radio or physical, with the vehicle until the mission
was completed [5].
Due to the highly experimental nature of the Urban Challenge
and the compressed schedule, most, if not all, teams performed a
tightly coupled “code → code while testing → code” iterative loop
of development. This practice was certainly true of the MIT team
and left little room for evaluating the effects of constant software
changes on the overall performance of the system. In other words,
the team was continuously writing software with no formal process
for updating the software on the physical system. Therefore,
87

while the vehicles met the goals set forth by DARPA for operator
interaction, we estimate the level of verification on each vehicle
was very low, as shown in Figure 6. This highlights the large gap that
exists between a demonstration system that drives in a controlled
environment and a deployable mass-market or military system.
As described in the previous section, the software on Urban
Challenge vehicles was constantly creating and executing new
motion plans. However, this capability implies that tests must
adequately verify the performance of a system that does not have
a finite set of output actions based on a single set of inputs. This
verification discussion is beyond the scope of this paper, but is of
great interest to Draper Laboratory and will continue to be an area
of research for many.
Remotely Operated Systems
Most, if not all, currently deployed military and law enforcement
“robots” or “unmanned systems” are truly operated remotely. A
human operator at a terminal is providing frequent to continuous
input commands to the system to accomplish a mission. While
it is certainly required to verify that these systems perform their
functions, that verification testing process can focus on the accurate
execution of the operator commands. We therefore consider
remotely operated systems at the lowest end of the verification
effort scale, certainly nonzero, but far from the aircraft avionics
case. It is possible, however, that some unmanned aircraft systems
will execute automated flights back to home base on certain failure
conditions. Therefore, those systems would likely need verification
levels consummate with aircraft autopilot systems.
Communications Bandwidth
The expected interactions of the operator with the system also
have a direct effect on how much data must be exchanged between
the operator and the system during the mission. Higher operator
interaction will drive more bandwidth requirements, while low
interactions will save bandwidth, but increase the required
verification effort.
Urban Challenge Autonomous System
As the minimum case, we have the Urban Challenge type
autonomous vehicles, which were required to have only a dedicated
“emergency-stop” transceiver active during the race. This radio
allowed the race monitoring center to remotely pause or completely
disable any vehicle on the course, as well as give those same options
to the dedicated chase car drivers that were following each vehicle
around the course [5]. This kind of link did not exchange much
information; the GPS coordinates of the vehicle and some bits to
indicate current operating mode were sufficient. Therefore, we can
locate the bandwidth requirements for these vehicles on the very
low end of the scale as shown in Figure 6.
Remotely Operated Systems
At the opposite end of the scale, we have systems that are
representative of all the actively deployed “robotic” or “unmanned”
systems used in military operations. These systems are remotely
operated, requiring a constant high-bandwidth data link to a
ground station that allows an operator to see live video and other
system data at all times. These types of links are required to satisfy
the human operator’s need for rapidly updating data to operate
88
the system safely. Therefore, we place these systems highest on the
bandwidth requirement.
Operator Interaction Test Concepts
With an understanding of how the operators are expected to
interact with the system, the performance of the system with regard
to this metric can be measured directly. At all times during any
system-level tests, the actions of the operators must be recorded
and compared against the expected values.
During the Urban Challenge, we observed that many teams had
dedicated vehicle test drivers. These drivers had over months of
involvement become comfortable with what level of help would be
required for their vehicle for many scenarios. A practiced vehicle
test driver would allow the autonomous system to proceed in
situations a less experienced test driver would deem dangerous
and take control of the vehicle. This observation is an example of
how different drivers trusted the systems they interacted with and it
highlights the need to understand this relationship.
To transition more autonomous systems into daily use, the time
for developing that trust must be shortened from months or weeks
into hours, or perhaps even minutes. All of us routinely estimate the
actions of others around us and trust that they will execute tasks
much as we would on a daily basis. When driving on a road, we all
assume that others around us are following the rules of that road as
expected; we routinely trust our lives to complete strangers.
Indeed, it is a daunting task to conjecture what will be required to
ever achieve the verification of a completely autonomous vehicle
driving in a general city setting. Aircraft avionics benefit from a very
strict operating set of conditions and intentional air traffic control to
mitigate the chance of collisions, but ground vehicles have no such
aids and operate in a far more complex and dynamic environment.
Level of Assertiveness
Finally, we discuss the idea of an autonomous system being
assertive: How much leeway should the system be given in
executing the desired mission? This is another characteristic that is
entirely controllable by the customer and the development team.
It is inversely related to the previously discussed frequency of
operator interaction in that a system intended to operate for long
periods without assistance must necessarily be assertive in mission
execution.
The intent of specifying assertiveness is to give the operators and
collaborating humans a feel for how persistent a given system will
be in completing the mission. This “feel” may be a time span over
which the system “thinks” about different options for continuing
a mission in the face of an obstacle, and it may include various
physical behaviors that allow the system to scan the situation with
the perceptual system from a different viewpoint in order to resolve
a perceived ambiguity in what the system is seeing.
Object Classification Accuracy
We feel that for an autonomous system to be assertive in executing
a mission, it must be able to not only see obstacles in the path of
the vehicle, it must be able to classify what those obstacles are. For
example, if a truck-sized LIDAR-equipped ground vehicle encounters
a long row of low bushes, it will “see” these bushes as a point cloud of
Requirements-Driven Autonomous System Test Design: Building Trusting Relationships

distance measurements with a regular height. Those bushes, for all
practical purposes, will look exactly like a concrete wall to a LIDAR,
and the vehicle will be confronted with a planning decision: find a
way around this obstacle or drive over it. To an outside observer, this
decision is trivial (assuming the property owner is not around), but
it is a real and difficult problem in autonomous system deployment.
The DARPA Urban Challenge mitigated the issue of object
classification by carefully selecting the rules to make the problem
more tractable. For example, rules dictated that the race course
would only contain other cars and static objects such as traffic
cones or barrels. The distinction between static objects and cars
was important due to different standoff distance requirements for
the two types of objects. Vehicles were allowed to pass much closer
to static objects (including parked cars) than moving vehicles.
In the case of Talos, the classification of objects was performed
based solely on the tracked velocity of the object. This type of
classification avoided the need to attempt to extract vehicle
specific geometry from LIDAR and camera data, but also
contributed to a low-speed collision with the Cornell system [17].
Unlike previous system characteristics, we only have the Urban
Challenge example, but we feel qualitative curves can still
be constructed to show a relationship between classification
accuracy and assertiveness as shown in Figure 7. Notice that we
also feel that the need to increase levels of classification accuracy
is a function of the environment uncertainty: Systems that operate
in a low uncertainty environment can be very assertive with a low
level of classification accuracy.
At the lowest end of the scale, we place a zero assertiveness
system: It will never change the operating plan without interaction
from an operator because the operator is making all classification
decisions. Examples of zero assertiveness systems are remotely
operated robots and aircraft autopilots, both of which require
operator interaction to change plans.
We estimate most Urban Challenge systems have low classification
accuracy in a moderately uncertain environment. Based on
experience with the Talos system, we estimate that it classified
objects correctly around 20% of the time, and the assertiveness
was intentionally skewed toward the far end of the scale, but the
system would eventually stop all motion if no safe motion plans
were found.
Finally, we include a not quite 100% rating for human classification
accuracy for the most uncertain environments at the “never ask
for help” end of the scale.
As shown, we feel that there is much work remaining to achieve
practical autonomous systems that can complete missions in
uncontrolled environments without a well-defined method of
operators assisting that system.
Assertiveness Test Concepts
Object classification was an important part of the Urban
Challenge testing processes. Test scenarios were developed to
intentionally provide a mixed set of vehicle and static obstacles
during development. Other team members (and even other Urban
Challenge systems) provided live traffic vehicles in a representative
Requirements-Driven Autonomous System Test Design: Building Trusting Relationships
Classification Accuracy (%)
Classification Accuracy with Level of Assertiveness
100
Human
80
60
40
20
0
Stop & Wait for Input
Urban Challenge
Increasing
Environment
Uncertainty
Aircraft Autopilot
Assertiveness Never Ask for Help
Figure 7. Variation of classification accuracy as a function of
assertiveness.
“urban” environment at the former El Toro Marine Corps Air Station
in Irvine, California.
Another valuable source of classification data came from the
human-driven commuting times to and from test sites. The software
architecture of the Talos system allowed real-time recording of all
system data that could be played back later. This allowed algorithm
testing with real system data on any developer computer [3]. The
vehicle perception systems were often left operating in many
types of general traffic scenarios that were later used to evaluate
classification performance.
Testing for assertiveness did not happen until near the end of
the Urban Challenge project for the MIT team as it was a concept
that grew out of testing just prior to the race. The Talos team and
others [10], [3] implemented logic into the systems designed to
“unstick” the vehicles and continue on the mission. In order to test
these features, the test designer must have a working knowledge
of how the assertiveness of the system should vary as a function of
operating conditions.
When the Talos vehicle failed to make forward progress, a chain of
events would start increasing the assertiveness level of the system
incrementally. This was done by relaxing planning constraints that
the vehicle was maintaining, such as staying within the lane or road
boundaries and large standoff distances to objects. This gave the
planning system a chance to recalculate a plan that may result in
forward progress. These relaxations of constraints would escalate
until eventually, if no plan were found, the system would reboot all
the control software and try again [3].
Conclusions
We have proposed and given examples for how to categorize the toplevel
requirements for the performance of an autonomous system.
These characteristics are intended to apply to any automated/
intelligent/autonomous system by describing expected behaviors
that in turn specify the required performance on lower level system
capabilities, thereby providing a basis for testing and analysis.
89

Environmental uncertainty is the primary driver for the overall
perceptual needs of the system. Systems that operate in highly
uncertain environments must be able to recognize and react to
objects from any direction at a response time sufficient to avoid
collisions. Estimating a metric of perception coverage reveals
that current state-of-the-art LIDAR systems provide far less
perception information than human vision and provide seconds
or less in collision detection range, but depending on the required
operational environment may be sufficient. Testing the system for
varying levels of environment uncertainty must be a focus of any
autonomous system verification; experience indicates that groundbased
systems in particular are very sensitive to environmental
uncertainty.
The frequency of operator interaction is a controlling parameter
that has a direct effect on several key system abilities: planner
complexity, verification effort, and communications bandwidth.
Motion planning systems capable of continuously creating new plans
in response to environment changes are inherently a nonfinite state
and therefore need new types of verification testing and research.
When a system is intended to operate with minimal operator input,
it allows the communications bandwidth to be reduced, whereas
teleoperated systems with constant operator interaction require
more robust links.
And finally, the level of assertiveness of the system, which is tied to
the desired frequency of operator interaction, will have an impact
on how accurately the autonomous system must be able to classify
objects in the environment. Systems that are intended to operate
with little supervision must make safe decisions about crossing
perceived constraints of travel in the environment, which drives the
need to classify objects around the system. Object classification is a
complex topic that requires much research to create robust systems.
Specifying an assertive autonomous system also requires a planning
system that is allowed to change motion plans automatically during
the mission, driving up the planner complexity and the associated
verification efforts.
Draper Laboratory will continue efforts to refine these characteristics
(and expand if needed) and is interested in collaborating with
other institutions in developing requirements and test metrics for
autonomous systems. We believe it will take widespread agreement
among different organizations to arrive at an understandable set of
guidelines that will help move advanced autonomous systems into
fielded use domestically and in military operations. These systems,
even with limitations of current perception and planning, can be
useful right now in reducing threats to U.S. military forces. We must
focus efforts on specifying and testing systems that can be trusted
by their operators to succeed in their missions.
References
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[2] Sternberg, S., “Memory Scanning: Mental Processes Revealed by
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Requirements-Driven Autonomous System Test Design: Building Trusting Relationships

Troy B. Jones is the Autonomous Systems Capability Leader at Draper Laboratory. He joined Draper in 2004
and began working in the System Integration and Test division on the TRIDENT MARK 6 MOD 1 inertial guidance
system. Current duties focus on strengthening Draper’s existing autonomous technologies in platform design and
software by adding new testing methods and incorporating concepts from Human System Collaboration. Draper’s
ultimate goal is to produce autonomous systems that are trusted implicitly by our customers to perform their
critical missions. In 2006, he joined with students and faculty at MIT to build an entry for the 2007 DARPA Urban
Challenge. The team’s fully autonomous Land Rover LR3 used a combination of LIDAR, vision, radar, and GPS/INS
to perceive the environment and road, and safely completed the Urban Challenge in fourth place overall. Mr. Jones
earned B.S. and M.S. degrees at Virginia Tech.
Mitch G. Leammukda is a Member of the Technical Staff in the Integrated Systems Development and
Test group at Draper Laboratory. For the past 7 years, he has worked on navigation systems for naval aircraft,
space instruments, and individual soldiers. He has also led the system integration for a robotic forklift and an
RF instrumentation platform. He is currently developing a universal test station platform for inertial guidance
instruments. Mr. Leammukda holds M.S. and B.S. degrees in Electrical Engineering from Northeastern University.
Requirements-Driven Autonomous System Test Design: Building Trusting Relationships
91

92
List of 2010 Published Papers and Presentations
Abrahamsson, C.K.; Yang, F.; Park, H.; Brunger, J.M.; Valonen, P.K.;
Langer, R.S.; Welter, J.F.; Caplan, A.I.; Guilak, F.; Freed, L.E.
Chondrogenesis and Mineralization During In Vitro Culture of
Human Mesenchymal Stem Cells on 3D-Woven Scaffolds
Tissue Engineering: Part A, Vol. 16, No. 7, July 2010
Abramson, M.R.; Kahn, A.C.; Kolitz, S.E.
Coordination Manager - Antidote to the Stovepipe Anti-Pattern
Infotech at Aerospace Conference, Atlanta, GA, April 20-22, 2010.
Sponsored by: AIAA
Abramson, M.R.; Carter, D.W.; Kahn, A.C.; Kolitz, S.E.; Riek, J.C.;
Scheidler, P.J.
Single Orbital Revolution Planner for NASA’s EO-1 Spacecraft
Infotech at Aerospace Conference, Atlanta, GA, April 20-22, 2010.
Sponsored by: AIAA
Agte, J.S.; Borer, N.K.; de Weck, O.
Simulation-Based Design Model for Analysis and Optimization of
Multistate Aircraft Performance
Multidisciplinary Design Optimization (MDO) Specialist’s Conference,
Orlando, FL, April 12-15, 2010. Sponsored by: AIAA
Ahuja, R.; Tao, S.L.; Nithianandam, B.; Kurihara, T.; Saint-Geniez, M.;
D’Amore, P.; Redenti, S.; Young, M.
Polymer Thin Films as an Antiangiogenic and Neuroprotective
Biointerface
Materials Research Society (MRS) Fall Meeting, Boston, MA, November
29-December 3, 2010. Sponsored by: MRS.
Ahuja, R.; Nithianandam, B.; Kurihara, T.; Saint-Geniez, M.; D’Amore, P.;
Redenti, S.; Young, M.; Tao, S.L
Polymer Thin-Films as an Antiangiogenic and Neuroprotective
Biointerface
Graduate Student Award Appreciation, Materials Research Society,
Boston, MA, November 2010
Barbour, N.M.; Hopkins III, R.E.; Kourepenis, A.S.; Ward, P.A.
Inertial MEMS System Applications (SET116)
NATO SET Lecture Series, Turkey, Czech Republic, France, Portugal,
March 15-26, 2010. Sponsored by: NATO Research & Technology
Organization
Barbour, N.M.
Inertial Navigation Sensors (SET116)
NATO SET Lecture Series, Turkey, Czech Republic, France, Portugal,
March 15-26, 2010. Sponsored by: NATO Research & Technology
Organization
List of 2010 Published Papers and Presentations
Barbour, N.M.; Flueckiger, K.
Understanding Commonly Encountered Inertial Instrument
Specifications
Missile Defense Agency/Deputy for Engineering, Producibility (MDA/
DEP), June 2010
Bellan, L.; Wu, D.; Borenstein, J.T.; Cropeck, D.; Langer, R.S.
Microfluidics in Hydrogels Using a Sealing Adhesion Layer
(poster)
Biomedical Engineering Society/Annals of Biomedical Engineering,
May 5, 2010
Benvegnu, E.; Suri, N.; Tortonesi, M.; Esterrich III, T.
Seamless Network Migration Using the Mockets Communications
Middleware
Military Communications Conference (MILCOM), San Jose, CA,
October 31-November 3, 2010. Sponsored by: IEEE
Bettinger, C.J.; Borenstein, J.T.
Biomaterials-Based Microfluidics for Tissue Development
Soft Matter, Vol. 6, No. 20, October 2010
Billingsley, K.L.; Balaconis, M.K.; Dubach, J.M.; Zhang, N.; Lim, E.;
Francis, K.; Clark, H.A.
Fluorescent Nano-Optodes for Glucose Detection
Analytical Chemistry, American Chemical Society (ACS), Vol. 82, No. 9,
May 1, 2010
Bogner, A.J.; Torgerson, J.F.; Mitchell, M.L.
GPS Receiver Development History for the Extended Navy Test Bed
Missile Sciences Conference, Monterey, CA, November 16-18, 2010.
Sponsored by: AIAA
Borenstein, J.T.; Tupper, M.M.; Mack, P.J.; Weinberg, E.J.; Khalil, A.S.;
Hsiao, J.C.; García-Cardeña, G.
Functional Endothelialized Microvascular Networks with Circular
Cross-Sections in a Tissue Culture Substrate
Biomedical Microdevices, Vol. 12, No. 1, February 2010
Borer, N.K.
Analysis and Design of Fault-Tolerant Systems
DEKA Lecture Series, Manchester, NH, August 12, 2010. Sponsored by:
DEKA Research and Development.
Borer, N.K.; Cohanim, B.E.; Curry, M.L.; Manuse, J.E.
Characterization of a Persistent Lunar Surface Science Network
Using On-Orbit Beamed Power
Aerospace Conference, Big Sky, MT, March 6-13, 2010. Sponsored by:
IEEE

Borer, N.K.; Claypool, I.R.; Clark, W.D.; West, J.J.; Odegard, R.G.;
Somervill, K.; Suzuki, N.
Model-Driven Development of Reliable Avionics Architectures for
Lunar Surface Systems
Aerospace Conference, Big Sky, MT, March 6-13, 2010. Sponsored by:
IEEE
Bortolami, S.B.; Duda, K.R.; Borer, N.K.
Markov Analysis of Human-in-the-Loop System Performance
Aerospace Conference, Big Sky, MT, March 6-13, 2010. Sponsored by:
IEEE
Brady, T.M.; Paschall II, S.C.
Challenge of Safe Lunar Landing
Aerospace Conference, Big Sky, MT, March 6-13, 2010. Sponsored by:
IEEE
Brady, T.M.; Paschall II, S.C.; Crain, T.
GN&C Development for Future Lunar Landing Missions
Guidance, Navigation, and Control Conference and Exhibit, Toronto,
Canada, August 2-5, 2010. Sponsored by: AIAA
Carter, D.J.; Cook, E.
Towards Integrated CNT-Bearing Based MEMS Rotary Systems
Gordon Research Conference on Nanostructure Fabrication, Tilton,
NH, July 18-23, 2010. Sponsored by: Tilton School
Clark, T.; Stimpson, A.; Young, L.R.; Oman, C.M.; Duda, K.R.
Analysis of Human Spatial Perception During Lunar Landing
Aerospace Conference, Big Sky, MT, March 6-13, 2010. Sponsored by:
IEEE
Cohanim, B.E.; Cunio, P.M.; Hoffman, J.; Joyce, M.; Mosher, T.J.; Tuohy, S.T.
Taking the Next Giant Leap
33rd Guidance and Control Conference, Breckenridge, CO, February
6-10, 2010. Sponsored by: AAS
Collins, B.K.; Kessler, L.J.; Benagh, E.A.
Algorithm for Enhanced Situation Awareness for Trajectory
Performance Management
Infotech at Aerospace Conference, Atlanta, GA, April 20-22, 2010.
Sponsored by: AIAA
Copeland, A.D.; Mangoubi, R.; Mitter, S.K.; Desai, M.N.; Malek, A.M.
Spatio-Temporal Data Fusion in Cerebral Angiography
IEEE Transactions on Medical Imaging, Vol. 29, No. 6, June 2010
Crain, T.; Bishop, R.H.; Brady, T.M.
Shifting the Inertial Navigation Paradigm with MEMS Technology
33rd Guidance and Control Conference, Breckenridge, CO, February
6-10, 2010. Sponsored by: American Astronautical Society (AAS)
List of 2010 Published Papers and Presentations
Cuiffi, J.D.; Soong, R.K.; Manolakos, S.Z.; Mohapatra, S.; Larson, D.N.
Nanohole Array Sensor Technology: Multiplexed Label-Free
Protein Binding Assays
26th Southern Biomedical Engineering Conference, College Park,
MD, April 30-May 2, 2010. Sponsored by: International Federation for
Medical and Biological Engineering (IFMBE)
Cunha, M.G.; Clarke, A.C.; Martin, J.; Beauregard, J.R.; Webb, A.K.;
Hensley, A.A.; Keshava, N.; Martin, D.J.
Detection of Deception in Structured Interviews Using Sensors
and Algorithms
International Society for Optical Engineers (SPIE) Defense, Security
and Sensing, Orlando, FL, April 5-9, 2010. Sponsored by: SPIE
Cunio, P.M.; Lanford, E.R.; McLinko, R.; Han, C.; Canizales-Diaz, J.;
Olthoff, C.T.; Nothnagel, S.L.; Bailey, Z.J.; Hoffman, J.; Cohanim, B.E.
Further Development and Flight Testing of a Prototype Lunar
and Planetary Surface Exploration Hopper: Update on the
TALARIS Project
Space 2010 Conference, Anaheim, CA, August 30-September 3, 2010.
Sponsored by: AIAA
Cunio, P.M.; Corbin, B.A.; Han, C.; Lanford, E.R.; Yue, H.K.; Hoffman, J.;
Cohanim, B.E.
Shared Human and Robotic Landing and Surface Exploration in
the Neighborhood of Mars
Space 2010 Conference, Anaheim, CA, August 30-September 3, 2010.
Sponsored by: AIAA
Davis, J.L.; Striepe, S.A.; Maddock, R.W.; Johnson, A.E.; Paschall II, S.C.
Post2 End-to-End Descent and Landing Simulation for ALHAT
Design Analysis Cycle 2
International Planetary Probe Workshop, Barcelona, Spain, June 14-18,
2010. Sponsored by: Georgia Institute of Technology
DeBitetto, P.A.
Using 3D Virtual Models and Ground-Based Imagery for Aiding
Navigation in Large-Scale Urban Terrain
35th Joint Navigation Conference (JNC), Orlando, FL, June 8-10, 2010.
Sponsored by: Joint Services Data Exchange (JSDE)
Dorland, B.N.; Dudik, R.P.; Veillette, D.; Hennessy, G.S.; Dugan, Z.; Lane,
B.F.; Moran, B.A.
Automated Frozen Sample Aliquotting System
European Laboratory Robotics Interest Group (ELRIG) Liquid
Handling & Label-Free Detection Technologies Conference,
Whittlebury Hall, UK, March 4, 2010. Sponsored by: ELRIG
93

Dorland, B.N.; Dudik, R.P.; Veillette, D.; Hennessy, G.S.; Dugan, Z.; Lane,
B.F.; Moran, B.A.
The Joint Milli-Arcsecond Pathfinder Survey (JMAPS):
Measurement Accuracy of the Primary Instrument when Used as
Fine Guidance Sensor
33rd Guidance and Control Conference, Breckenridge, CO, February
6-10, 2010. Sponsored by: AAS
Dubach, J.M.; Lim, E.; Zhang, N.; Francis, K.; Clark, H.A.
In Vivo Sodium Concentration Continuously Monitored with
Fluorescent Sensors
Integrative Biology: Quantitative Biosciences from Nano to Macro,
November 2010
Duda, K.R.; Johnson, M.C.; Fill, T.J.; Major, L.M.; Zimpfer, D.J.
Design and Analysis of an Attitude Command/Hover Hold plus
Incremental Position Command Blended Control Mode for Piloted
Lunar Landing
Guidance, Navigation, and Control Conference and Exhibit, Toronto,
Canada, August 2-5, 2010. Sponsored by: AIAA
Duda, K.R.; Oman, C.M.; Hainley Jr., C.J.; Wen, H.-Y.
Modeling Human-Automation Interactions During Lunar Landing
Supervisory Control
81st Annual Aerospace Medical Association (ASMA) Scientific
Meeting, Phoenix, AZ, May 9-13, 2010. Sponsored by: ASMA
Effinger, R.T.; Williams, B.; Hofmann, A.
Dynamic Execution of Temporally and Spatially Flexible Reactive
Programs
24th Association for the Advancement of Artificial Intelligence (AAAI)
Conference on Artificial Intelligence, Atlanta, GA, July 11-15, 2010.
Sponsored by: AAAI
Epshteyn, A.A.; Maher, S.P.; Taylor, A.J.; Borenstein, J.T.; Cuiffi, J.D.
Membrane-Integrated Microfluidic Device for High-Resolution
Live Cell Imaging Fabricated via a Novel Substrate Transfer
Technique
Materials Research Society (MRS) Fall Meeting, Boston, MA, November
29-December 3, 2010. Sponsored by: MRS
Fallon, L.P.; Magee, R.J.; Wadland, R.A.
Centrifuge Technologies for Evaluating Inertial Guidance Systems
81st Shock and Vibration Symposium, Orlando, FL, October 24-28,
2010. Sponsored by: Shock and Vibration Information Analysis Center
(SAVIAC)
Feng, M.Y.; Marinis, T.F.; Giglio, J.; Sherman, P.G.; Elliott, R.D.; Magee, T.;
Warren, J.
Electronics Packaging to Isolate MEMS Sensors from Thermal
Transients
International Mechanical Engineering Congress, Vancouver, CA,
November 12-18, 2010. Sponsored by: ASME
94
List of 2010 Published Papers and Presentations
Fill, T.J.
Lunar Landing and Ascent Trajectory Guidance Design for
the Autonomous Landing and Hazard Avoidance Technology
(ALHAT) Program
Space Flight Mechanics Conference, San Diego, CA, February 14-17,
2010. Sponsored by: AAS and AIAA
Fritz, M.P.; Zanetti, R.; Vadali, S.R.
Analysis of Relative GPS Navigation Techniques
Space Flight Mechanics Conference, San Diego, CA, February 14-17,
2010. Sponsored by: AAS and AIAA
Frohlich, E.; Ko, C.W.; Tao, S.L.; Charest, J.L.
Fabrication of Cell Substrates to Determine the Role of Mechanical
Cues in Tissue Structure Formation of Renal Epithelial Cells
Science and Engineering Day Symposium, Boston, MA, March 30,
2010. Sponsored by: Boston University
Frohlich, E.; Ko, C.W.; Zhang, X.; Charest, J.L.; Tao, S.L.
Fabrication of Cell Substrates to Determine the Role of
Topographical Cues in Differentiation and Tissue Structure
Formation
Tech Connect Summit, Anaheim, CA, June 21-24, 2010. Sponsored by:
TechConnect World
Geisler, M.A.
Expedition MCM-D Layout for Multi-Layer Die
User2User (U2U) Mentor Graphics Users Conference, Westford, MA,
April 14, 2010. Sponsored by: U2U
Grant, M.J.; Steinfeldt, B.A.; Braun, R.D.; Barton, G.H.
Smart Divert: A New Mars Robotic Entry, Descent, and Landing
Architecture
Journal of Spacecraft and Rockets, AIAA, Vol. 47. No. 3, May-June 2010
Guillemette, M.D.; Park, H.; Hsiao, J.C.; Jain, S.R.; Larson, B.L.; Langer,
R.S.; Freed, L.E.
Combined Technologies for Microfabricating Elastomeric Cardiac
Tissue Engineering Scaffolds
Journal of Macromolecular Bioscience, Vol. 10, No. 11, November 2010
Guo, X.; Popadin, K.Y.; Markuzon, N.; Orlov, Y.L.; Kraytsberg, Y.;
Krishnan, K.J.; Zsurka, G.; Turnbull, D.M.; Kunz, W.S.; Khrapko, K.
Repeats, Longevity, and the Sources of mtDNA Deletions:
Evidence from “Deletional Spectra”
Trends in Genetics, Vol. 26, No. 8, August 2010, pp. 340-343
Hammett, R.C.
Fault-Tolerant Avionics Tutorial for the NASA/Army Forum on
“Challenges of Complex Systems”
NASA/Army Systems and Software Engineering Forum, Huntsville, AL,
May 11-12, 2010. Sponsored by: University of Alabama

Harjes, D.I.; Dubach, J.M.; Rosenzweig, A.; Das, S.; Clark, H.A.
Ion-Selective Optodes Measure Extracellular Potassium Flux in
Excitable Cells
Macromolecular Rapid Communications, Vol. 31, No. 2, January 2010
Herold, T.M.; Abramson, M.R.; Kahn, A.C.; Kolitz, S.E.; Balakrishnan, H.
Asynchronous, Distributed Optimization for the Coordinated
Planning of Air and Space Assets
Infotech at Aerospace Conference, Atlanta, GA, April 20-22, 2010.
Sponsored by: AIAA
Hicks, B.; Cook, T.; Lane, B.F.; Chakrabarti, S.
OPD Measurement and Dispersion Reduction in a Monolithic
Interferometer
Optics Express, Vol. 18, No. 16, August 2, 2010, pp. 17542-17547
Hicks, B.; Cook, T.; Lane, B.F.; Chakrabarti, S.
Progress in the Development of MANIC: a Monolithic Nulling
Interferometer for Characterizing Extrasolar Environments
Astronomical Telescopes and Instrumentation, San Diego, CA, June
27-July 2, 2010. Sponsored by: SPIE
Hoganson, D.M.; Anderson, J.L.; Weinberg, E.J.; Swart, E.F.; Orrick, B.;
Borenstein, J.T.; Vacanti, J.P.
Branched Vascular Network Architecture: A New Approach to
Lung Assist Device Technology
Journal of Thoracic and Cardiovascular Surgery, Vol. 140, No. 5,
November 2010
Hopkins III, R.E.
Contemporary and Emerging Inertial Sensor Technologies
Position Location and Navigation Symposium (PLANS), Indian Wells,
CA, May 4-6, 2010. Sponsored by: IEEE/Institute of Navigation (ION)
Hopkins III, R.E.; Barbour, N.M.; Gustafson, D.E.; Sherman, P.G.
Integrated Inertial/GPS-Based Navigation Applications
NATO SET Lecture Series, Turkey, Czech Republic, France, Portugal,
March 15-26, 2010 Sponsored by: NATO Research & Technology
Organization
Hsiao, J.C.; Borenstein, J.T.; Kulig, K.M.; Finkelstein, E.B.; Hoganson, D.M.;
Eng, K.Y.; Vacanti, J.P.; Fermini, B.; Neville, C.M.
Novel In Vitro Model of Vascular Injury with a Biomimetic Internal
Elastic Lamina
TERMIS-NA Annual Conference & Exposition, Orlando, FL, December
5-10, 2010. Sponsored by: Tissue Engineering International and
Regenerative Medicine Society
Hsu, W.-M.; Carraro, A.; Kulig, K.M.; Miller, M.L.; Kaazempur-Mofrad,
M.R.; Entabi, F.; Albadawi, H.; Watkins, M.T.; Borenstein, J.T.; Vacanti, J.P.;
Neville, C.M.
Liver Assist Device with a Microfluidics-Based Vascular Bed in an
Animal Model
Annals of Surgery, Vol. 252, No. 2, August 2010
List of 2010 Published Papers and Presentations
Huxel, P.J.; Cohanim, B.E.
Small Lunar Lander/Hopper Navigation Analysis Using Linear
Covariance
Aerospace Conference, Big Sky, MT, March 6-13, 2010. Sponsored by:
IEEE
Irvine, J.M.
ATR Technology: Why We Need It, Why We Can’t Have It, and How
We’ll Get It
Geotech Conference, Fairfax, VA, September 27-28, 2010. Sponsored
by: American Society of Photogrammetry and Remote Sensing
(ASPRS)
Irvine, J.M.
Human Guided Visualization Enhances Automated Target
Detection
SPIE Defense, Security and Sensing, Orlando, FL, April 5-9, 2010.
Sponsored by: SPIE
Jackson, M.C.; Straube, T.
Orion Flight Performance Design Trades
Guidance, Navigation, and Control Conference and Exhibit, Toronto,
Canada, August 2-5, 2010. Sponsored by: AIAA
Jackson, T.R.; Keating, D.J.; Mather, R.A.; Matlis, J.; Silvestro, M.; Ting, B.C.
Role of Modeling, Simulation, Testing, and Analysis Throughout
the Design, Development, and Production of the MARK 6 MOD 1
Guidance System
Missile Sciences Conference. Monterey, CA, November 16-18, 2010.
Sponsored by: AIAA
Jang, D.; Wendelken, S.M.; Irvine, J.M.
Robust Human Identification Using ECG: Eigenpulse Revisited
SPIE Defense, Security and Sensing, Orlando, FL, April 5-9, 2010.
Sponsored by: SPIE
Jang, J.-W.; Plummer, M.K.; Bedrossian, N.S.; Hall, C.; Spanos, P.D.
Absolute Stability Analysis of a Phase Plane Controlled Spacecraft
20th Spaceflight Mechanics Meeting, San Diego, CA, February 14-17,
2010. Sponsored by: AAS/AIAA
Jang, J.-W.; Alaniz, A.; Bedrossian, N.S.; Hall, C.; Ryan, S.; Jackson, M.
Ares I Flight Control System Design
2010 Astrodynamics Specialist Conference, Toronto, Canada, August
2-5, 2010. Sponsored by: AAS/AIAA
Jones, T.B.; Leammukda, M.G.
Requirements-Driven Autonomous System Test Design: Building
Trusting Relationships
International Test and Evaluation Association (ITEA) Live Virtual
Constructive Conference, El Paso, TX, January 11-14, 2010
Kahn, A.C.; Kolitz, S.E.; Abramson, M.R.; Carter, D.W.
Human-System Collaborative Planning Environment for
Unmanned Aerial Vehicle Mission Planning
Infotech at Aerospace Conference, Atlanta, GA, April 20-22, 2010.
Sponsored by: AIAA
95

Keating, D.J.; Laiosa, J.P.; Ting, B.C.; Wasileski, B.J.; Vican, J.E.; Silvestro,
M.; Foley, B.M.; Shakhmalian, C.T.
Using Hardware-in-the-Loop Simulation for System Integration of
the MARK 6 MOD 1 Guidance System
Missile Sciences Conference, Monterey, CA, November 16-18, 2010.
Sponsored by: AIAA
Keshava, N.
Detection of Deception in Structured Interviews Using Sensors
and Algorithms
SPIE Defense, Security and Sensing, Orlando, FL, April 5-9, 2010.
Sponsored by: SPIE
Keshava, N.; Coskren, W.D.
Sensor Fusion for Multi-Sensor Human Signals to Infer Cognitive
States
National Symposium Sensor Data Fusion, Las Vegas, NV, July 26-29,
2010. Sponsored by: Military Sensing Symposium
Kessler, L.J.; West, J.J.; McClung, K.; Miller, J.; Zimpfer, D.J.
Autonomous Operations for the Next Generation of Human Space
Exploration
SpaceOps, Huntsville, AL, April 25-30, 2010. Sponsored by: AIAA
Kim, K.H.; Burns, J.A.; Bernstein, J.J.; Maguluri, G.N.; Park, B.H.; De Boer, J.F.
In Vivo 3D Human Vocal Fold Imaging with Polarization Sensitive
Optical Coherence Tomography and a MEMS Scanning Catheter
Optics Express, Vol. 18, No. 14, July 5, 2010
King, E.T.; Hart, J.J.; Odegard, R.
Orion GN&C Data-Driven Flight Software Architecture for
Automated Sequencing and Fault Recovery
Aerospace Conference, Big Sky, MT, March 6-13, 2010. Sponsored by:
IEEE
Kniazeva, T.; Hsiao, J.C.; Charest, J.L.; Borenstein, J.T.
Microfluidic Respiratory Assist Device with High Gas Permeability
for Artificial Lung Applications
Biomedical Microdevices, Online First, November 26, 2010
Ko, C.W.; McHugh, K.J.; Yao, J.; Kurihara, T.; D’Amore, P.; Saint-Geniez, M.;
Young, M.; Tao, S.L.
Nanopatterning of Poly(e-caprolactone) Thin Film Scaffolds for
Retinal Rescue
4th Military Vision Symposium on Ocular and Brain Injury, Boston,
MA, September 26-30, 2010. Sponsored by: Schepens Eye Research
Institute
Ko, C.W.
Micro and Nanostructured Polymer Thin Films for the
Organization and Differentiation of Retinal Progenitor Cells
Materials Research Society Fall Meeting, Boston, MA, November
29-December 3, 2010. Sponsored by: MRS
96
List of 2010 Published Papers and Presentations
Kourepenis, A.S.
Emerging Navigation Technologies for Miniature Autonomous
Systems
Autonomous Weapons Summit and GNC Challenges for Miniature
Autonomous Systems Workshop, Fort Walton Beach, FL, October 25-
27,
2010. Sponsored by: ION
Lai, W.; Erdonmez, C.K.; Marinis, T.F.; Bjune, C.K.; Dudney, N.J.; Xu, F.;
Wartena, R.; Chiang, Y.-M.
Ultrahigh-Energy-Density Microbatteries Enabled by New
Electrode Architecture and Micropackaging Design
Advanced Materials, Vol. 22, No. 20, May 2010
Larson, D.N.; Slusarz, J.; Bellio, S.L.; Maloney, L.M.; Ellis, H.J.; Rifai, N.;
Bradwin, G.; de Dios, J.
Automated Frozen Sample Aliquotter
International Society of Biological and Environmental Respositories
(ISBER) Annual Meeting and Exhibits, Rotterdam, Netherlands, May
11-14, 2010. Sponsored by: ISBER
Larson, D.N.; Fiering, J.O.; Kowalski, G.J.; Sen, M.
Development of a Nanoscale Calorimeter: Instrument for
Developing Pharmaceutical Products
Innovative Molecular Analysis Technologies (IMAT) Conference, San
Francisco, CA, October 25-26, 2010. Sponsored by: National Cancer
Institute (NCI)
Larson, D.N.; Miranda, L.; Dederis, J.
Innovations in Biobanking-Related Engineering and Design: A
Novel Automated Methodology for Optimizing Banked Sample
Processing
ISBER Annual Meeting and Exhibits, Rotterdam, Netherlands, May 11-
14, 2010. Sponsored by: ISBER
Larson, D.N.
Nanohole Array for Protein Analysis
26th Southern Biomedical Engineering Conference, College Park, MD,
April 30-May 2, 2010. Sponsored by: IFMBE
Larson, D.N.
Nanohole Array Sensing
Biomedical Optics Workshop, Boston, MA, April 13, 2010. Sponsored
by: IEEE and Boston University
Larson, D.N.
New Method for Processing Banked Samples
Biospecimen Research Network (BRN) Symposium, Bethesda, MD,
March 24-25, 2010. Sponsored by: NCI
Larson, D.N.
Optimizing the Processing and Augmenting the Value of Critical
Banked Biological Specimens
Biorepositories Conference, Boston, MA, September 27-29, 2010

Larson, D. N.
Transitioning Research into Operations: A View from Healthcare
NASA Human Research Program Investigators’ Workshop, Houston,
TX, February 3-5, 2010. Sponsored by: NASA/NASA Space Biomedical
Research Institute (NSBRI)
Lim, S.; Lane, B.F.; Moran, B.A.; Henderson, T.C.; Geisel, F.A.
Model-Based Design and Implementation of Pointing and
Tracking Systems: From Model to Code in One Step
33rd Guidance and Control Conference, Breckenridge, CO, February
6-10, 2010. Sponsored by: AAS
Lowry, N.C.; Mangoubi, R.S.; Desai, M.N.; Sammak, P.J.
Nonparametric Segmentation and Classification of Small Size
Irregularly Shaped Stem Cell Nuclei Using Adjustable Windowing
7th International Symposium on Biomedical Imaging: From Nano to
Macro, Rotterdam, the Netherlands, April 14-17, 2010. Sponsored by:
IEEE
Madison, R.W.; Xu, Y.
Tactical Geospatial Intelligence from Full Motion Video
Applied Imagery Pattern Recognition Workshop, Washington, D.C.,
October 13-15, 2010. Sponsored by: IEEE
Magee, R.J.
Shock and Vibration Information Analysis Center (SAVIAC) Video
81st Shock and Vibration Symposium, Orlando, FL, October 24-28,
2010. Sponsored by: SAVIAC
Major, L.M.; Duda, K.R.; Zimpfer, D.J.; West, J.J.
Approach to Addressing Human-Centered Technology Challenges
for Future Space Exploration
Space 2010 Conference, Anaheim, CA, August 30-September 3, 2010.
Sponsored by: AIAA
Manolakos, S.Z.; Evans-Nguyen, T.G.; Postlethwaite, T.A.
Low Temperature Plasma Sampling for Explosives Detection in a
Handheld Prototype
Chemical and Biological Defense Science and Technology Conference,
Orlando, FL, November 15-19, 2010. Sponsored by: Defense Threat
Reduction Agency (DTRA)
Marchant, C.C.
Ares I Avionics Introduction
AIAA Webinar, Huntsville, AL, February 11, 2010. Sponsored by: AIAA
Marchant, C.C.
Ares I Avionics Introduction
NASA/Army Systems and Software Engineering Forum, Huntsville, AL,
May 11-12, 2010. Sponsored by: University of Alabama
Marinis, T.F.; Nercessian, B.
Hermetic Sealing of Stainless Steel Packages by Seam Seal Welding
43rd International Symposium on Microelectronics, Raleigh,
NC, October 31-November 4, 2010. Sponsored by: International
Microelectronics and Packaging Society (IMAPS)
List of 2010 Published Papers and Presentations
Mather, R.A.
Development and Simulation of a 4-Processor Virtual Guidance
System for the MARK 6 MOD 1 Program
Missile Sciences Conference, Monterey, CA, November 16-18, 2010.
Sponsored by: AIAA
Matlis, J.
Application of Instruction Set Simulator Technology for Flight
Software Development for the MARK 6 MOD 1 Program
Missile Sciences Conference, Monterey, CA, November 16-18, 2010.
Sponsored by: AIAA
Matranga, M.J.
Draper Multichip Modules for Space Applications
ChipSat Workshop, Providence, RI, February 18, 2010. Sponsored by:
Brown University
McCall, A.A.; Swan, E.E.; Borenstein, J.T.; Sewell, W.F.; Kujawa, S.G.;
McKenna, M.J.
Drug Delivery for Treatment of Inner Ear Disease: Current State of
Knowledge
Ear & Hearing, Vol. 31, January 2010
McHugh, K.J.; Teynor, W.A.; Saint-Geniez, M.; Tao, S.L.
High-Yield MEMS Technique to Fabricate Microneedles for Tissue
Engineering Applications
National Institute of Biomedical Imaging and Bioengineering Training
Grantees Meeting, Bethesda, MD, June 24-25, 2010. Sponsored by:
National Institutes of Health (NIH)
McHugh, J.; Tao, S.L.; Saint-Geniez, M.
Template Fabrication of a Nanoporous Polycaprolactone Thin-
Film for Retinal Tissue Engineering
Materials Research Society (MRS) Fall Meeting, Boston, MA, November
29-December 3, 2010. Sponsored by: MRS
McLaughlin, B.L.; Wells, A.C.; Virtue, S.; Vidal-Puig, A.; Wilkinson, T.D.;
Watson, C.J.E.; Robertson, P.A.
Electrical and Optical Spectroscopy for Quantitative Screening of
Hepatic Steatosis in Donor Livers
Physics in Medicine and Biology, Vol. 55, No. 22, November 2010
Mescher, M.J.; Kim, E.S.; Fiering, J.O.; Holmboe, M.E.; Swan, E.E.; Sewell,
W.F.; Kujawa, S.G.; McKenna, M.J.; Borenstein, J.T.
Development of a Micropump for Dispensing Nanoliter-Scale
Volumes of Concentrated Drug for Intracochlear Delivery
33rd Association for Research in Otolaryngology (ARO) Midwinter
Meeting, Anaheim, CA, February 6-11, 2010. Sponsored by: ARO
Middleton, A.; Paschall II, S.C.; Cohanim, B.E.
Small Lunar Lander/Hopper Performance Analysis
Aerospace Conference, Big Sky, MT, March 6-13, 2010. Sponsored by:
IEEE
97

Miotto, P.; Breger, L.S.; Mitchell, I.T.; Keller, B.; Rishikof, B.
Designing and Validating Proximity Operations Rendezvous and
Approach Trajectories for the Cygnus Mission
Astrodynamics Specialist Conference, Toronto, Canada, August 2-5,
2010. Sponsored by: AAS/AIAA
Mitchell, M.L.; Werner, B.; Roy, N.
Sensor Assignment for Collaborative Urban Navigation
35th Joint Navigation Conference, Orlando, FL, June 8-10, 2010.
Sponsored by: JSDE
Mohiuddin, S.; Donna, J.I.; Axelrad, P.; Bradley, B.
Improving Sensitivity, Time to First Fix, and Robustness of GPS
Positioning by Combining Signals from Multiple Satellites
35th Joint Navigation Conference, Orlando, FL, June 8, 2010-June 10,
2010. Sponsored by: JSDE
Muterspaugh, M.W.; Lane, B.F.; Kulkarni, S.R.; Konacki, M.; Burke, B.F.;
Colavita, M.M.; Shao, M.; Wiktorowicz, S.J.; Hartkopf, W.I.; O’Connell, J.;
Williamson, M.; Fekel, F.C.
The PHASES Differential Astrometry Data Archive: Parts I – V
Astronomical Journal, AAS, Vol. 140, No. 6, December 2010
Nelson, E.D.; Irvine, J.M.
Intelligent Management of Multiple Sensors for Enhanced
Situational Awareness
Applied Imagery Pattern Recognition Workshop, Washington, D.C.
October 13-15, 2010. Sponsored by: IEEE
Nothnagel, S.L.; Bailey, Z.J.; Cunio, P.M.; Hoffman, J.; Cohanim, B.E.;
Streetman, B.J.
Development of a Cold Gas Spacecraft Emulator System for the
TALARIS Hopper
Space 2010 Conference, Anaheim, CA, August 30-September 3, 2010.
Sponsored by: AIAA
Olthoff, C.T.; Cunio, P.M.; Hoffman, J.; Cohanim, B.E.
Incorporation of Flexibility into the Avionics Subsystem for the
TALARIS Small Advanced Prototype Vehicle
Space 2010 Conference, Anaheim, CA, August 30-September 3, 2010.
Sponsored by: AIAA
O’Melia, S.; Elbirt, A.J.
Enhancing the Performance of Symmetric-Key Cryptography via
Instruction Set Extensions
IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Vol.
18, No. 11, November 2010
Okerson, G.; Kang, N.; Ross, J.; Tetewsky, A.K.; Soltz, J.; Greenspan, R.L.;
Anszperger, J.C.; Lozow, J.B.; Mitchell, M.R.; Vaughn, N.L.; O’Brien, C.P.;
Graham, D.K.
Qualitative and Quantitative Inter-Signal Correction Metrics for
On Orbit GPS Satellites
35th Joint Navigation Conference, Orlando, FL, June 8-10, 2010.
Sponsored by: JSDE
98
List of 2010 Published Papers and Presentations
Perry, H.C.; Polizzotto, L.; Schwartz, J.L.
Creative Path from Invention to Successful Transition
Aerospace Conference, Big Sky, MT, March 6-13, 2010. Sponsored by:
IEEE
Polizzotto, L.
Creating Customer Value Through Innovation
Technology & Innovation, Vol. 12, No. 1, January, 2010
Putnam, Z.R.; Barton, G.H.; Neave, M.D.
Entry Trajectory Design Methodology for Lunar Return
Aerospace Conference, Big Sky, MT, March 6-13, 2010. Sponsored by:
IEEE
Putnam, Z.R.; Neave, M.D.; Barton, G.H.
PredGuid Entry Guidance for Orion Return from Low Earth Orbit
Aerospace Conference, Big Sky, MT, March 6-13, 2010. Sponsored by:
IEEE
Rachlin, Y.; McManus, M.F.; Yu, C.C.; Mangoubi, R.S.
Outlier Robust Navigation Using L1 Minimization
35th Joint Navigation Conference, Orlando, FL, June 7-10, 2010.
Sponsored by: JSDE
Roy, W.A.; Kwok, P.Y.; Chen, C.-J.; Racz, L.M.
Thermal Management of a Novel iUHD-Technology-Based MCM
IMAPS National Meeting, Palo Alto, CA, September 28-30, 2010.
Sponsored by: IMAPS
Schaefer, M.L.; Wongravee, K.; Holmboe, M.E.; Heinrich, N.M.; Dixon,
S.J.; Zeskind, J.E.; Kulaga, H.M.; Brereton, R.G.; Reed, R.R.; Trevejo, J.M.
Mouse Urinary Biomarkers Provide Signatures of Maturation,
Diet, Stress Level, and Diurnal Rhythm
Chemical Senses, Vol. 35, No. 6, July 2010
Serna, F.J.
Systems Engineering Considerations in Practicing Test and
Evaluation
26th Annual National Test and Evaluation Conference, San Diego,
CA, March 1-4, 2010. Sponsored by: National Defense Industrial
Association (NDIA)
Sherman, P.G.
Precision Northfinding INS with Low-Noise MEMS Inertial Sensors
Joint Precision Azimuth Sensing Conference (JPASC), Las Vegas, NV,
August 2-6, 2010
Sievers, A.; Zanetti, R.; Woffinden, D.C.
Multiple Event Triggers in Linear Covariance Analysis for
Spacecraft Rendezvous
Guidance, Navigation, and Control Conference and Exhibit, Toronto,
Canada, August 2-5, 2010. Sponsored by: AIAA

Silvestro, M.
Time Synchronization in Closed-Loop GPS/INS Hardware-in-the-
Loop Simulations
35th Joint Navigation Conference, Orlando, FL, June 7-10, 2010.
Sponsored by: JSDE
Smith, B.R.; Kwok, P.Y.; Thompson, J.C.; Mueller, A.J.; Racz, L.M.
Demonstration of a Novel Hybrid Silicon-Resin High-Density
Interconnect (HDI) Substrate
60th Electronic Components and Technology Conference (ECTC), Las
Vegas, NV, June 1-4, 2010. Sponsored by IEEE, Components, Packaging
and Manufacturing Technology (CPMT) Society
Sodha, S.; Wall, K.A.; Redenti, S.; Klassen, H.; Young, M.; Tao, S.L.
Microfabrication of a Three-Dimensional Polycaprolactone Thin-
Film Scaffold for Retinal Progenitor Cell Encapsulation
Journal of Biomaterials Science - Polymer Edition, Vol. 22, No. 4-6,
January 2011
Stanwell, P.; Siddall, P.; Keshava, N.; Cocuzzo, D.C.; Ramadan, S.; Lin, A.;
Herbert, D.; Craig, A.; Tran, Y.; Middleton, J.; Gautam, S.; Cousins, M.;
Mountford, C.
Neuro Magnetic Resonance Spectroscopy Using Wavelet
Decomposition and Statistical Testing Identifies Biochemical
Changes in People with Spinal Cord Injury and Pain
Neuroimage, Vol. 53, No. 2, November 2010
Steedman, M.R.; Tao, S.L.; Klassen, H.; Desai, T.A.
Enhanced Differentiation of Retinal Progenitor Cells Using
Microfabricated Topographical Cues
Biomedical Microdevices, Vol. 12, No. 3, June 2010
Steinfeldt, B.A.; Grant, M.J.; Matz, D.A.; Braun, R.D.; Barton, G.H.
Guidance, Navigation, and Control System Performance Trades
for Mars Pinpoint Landing
Journal of Spacecraft and Rockets, AIAA, Vol. 47, No. 1, 2010
Steinfeldt, B.A.; Braun, R.D.; Paschall II, S.C.
Guidance and Control Algorithm Robustness Baseline Indexing
Guidance, Navigation, and Control Conference and Exhibit, Toronto,
Canada, August 2-5, 2010. Sponsored by: AIAA
Streetman, B.J.; Peck, M.A.
General Bang-Bang Control Method for Lorentz Augmented Orbits
Journal of Spacecraft and Rockets, AIAA, Vol. 47, No. 3, May-June 2010
Streetman, B.J.; Johnson, M.C.; Kroehl, J.F.
Generic Framework for Spacecraft GN&C Emulation: Performing a
Lunar-Like Hop on the Earth
Guidance, Navigation, and Control Conference and Exhibit, Toronto,
Canada, August 2-5, 2010. Sponsored by: AIAA
List of 2010 Published Papers and Presentations
Swan, E.E.; Borenstein, J.T.; Fiering, J.O.; Kim, E.S.; Mescher, M.J.; Murphy,
B.; Tao, S.L.; Chen, Z.; Kujawa, S.G.; McKenna, M.J.; Sewell, W.F.
Characterization of Reciprocating Flow Parameters for Inner Ear
Drug Delivery
33rd Midwinter Meeting, Association for Research in Otolaryngology,
Anaheim, CA, February 6-11, 2010. Sponsored by: ARO
Tamblyn, S.; Henry, J.R.; King, E.T.
Model-Based Design and Testing Approach for Orion GN&C Flight
Software Development
Aerospace Conference, Big Sky, MT, March 6-13, 2010. Sponsored by:
IEEE
Tao, S.L.
Polycaprolactone Nanowires for Controlling Cell Behavior at the
Biointerface
Popat, K., ed., Nanotechnology in Tissue Engineering and Regenerative
Medicine, Chapter 3, CRC Press, Taylor & Francis Group, Boca Raton,
FL, November 22, 2010
Tepolt, G.B.; Mescher, M.J.; LeBlanc, J.; Lutwak, R.; Varghese, M.
Hermetic Vacuum Sealing of MEMS Devices Containing Organic
Components
Photonics West-MOEMS-MEMS, San Francisco, CA, January 22-27,
2010. Sponsored by: SPIE
Torgerson, J.F.; Sherman, P.G.; Scudiere, J.D.; Tran, V.; Del Colliano, J.;
Sokolowski, S.; Ganop, S.
Collaborative Soldier Navigation Study
35th Joint Navigation Conference, Orlando, FL, June 7-10, 2010.
Sponsored by: JSDE
Tucker, J.; Boydston, T.E.; Heffner, K.
Closing the Level 4 Secure Computing Gap via Advanced MCM
Technology
Department of Defense Anti-Tamper Conference, Baltimore, MD, April
13-15, 2010. Sponsored by: DoD
Tucker, B; Saint-Geniez, M.; Tao, S.L.; D’Amore, P.; Borenstein, J.T.;
Herman, I.M.; Young, M.
Tissue Engineering for the Treatment of AMD
Expert Reviews in Ophthalmology, Vol. 5, No. 5, October 2010
Valonen, P.K.; Moutos, F.T.; Kusanagi, A.; Moretti, M.; Diekman, B.O.;
Welter, J.F.; Caplan, A.I.; Guilak, F.; Freed, L.E.
In Vitro Generation of Mechanically Functional Cartilage Grafts
Based on Adult Human Stem Cells and 3D-woven Poly(εcaprolactone)
Scaffolds
Biomaterials, Vol. 31, January 2010
99

Varsanik, J.S.; Teynor, W.A.; LeBlanc, J.; Clark, H.A.; Krogmeier, J.; Yang,
T.; Crozier, K.; Bernstein, J.J.
Subwavelength Plasmonic Readout for Direct Linear Analysis of
Optically Tagged DNA
Photonics West-BIOS, San Francisco, CA, January 23-28, 2010.
Sponsored by: SPIE
Wen, H.-Y.; Duda, K.R.; Oman, C.M.
Simulating Human-Automation Task Allocations for Space
System Design
Human Factors and Ergonomic Society Student Conference, New
England Chapter, Boston, MA., October 22, 2010
Wang, J.; Bettinger, C.J.; Langer, R.S.; Borenstein, J.T.
Biodegradable Microfluidic Scaffolds for Tissue Engineering
from Amino Alcohol-Based Poly(Ester Amide) Elastomers
Organogenesis, Volume 6, No. 4, 2010, pp. 1-5
Yoon, S.-H.; Cha, N.-G.; Lee, J.S.; Park, J.-G.; Carter, D.J.; Mead, J.L.; Barry,
C.M.F.
Effect of Processing Parameters, Antistiction Coatings, and
Polymer Type when Injection Molding Microfeatures
Polymer Engineering & Science, Vol. 50, Issue 2, February 2010
Yoon, S.-H.; Lee, K.-H.; Palanisamy, P.; Lee, J.S.; Cha, N.-G.; Carter, D.J.;
Mead, J.L.; Barry, C.M.F.
Enhancement of Surface Replication by Gas Assisted
Microinjection Moulding
Plastics, Rubber and Composites, Vol. 39, No. 7, September 2010
100
List of 2010 Published Papers and Presentations
Young, L.R.; Oman, C.M.; Stimpson, A.; Duda, K.R.; Clark, T.
Flight Displays and Control Modes for Safe and Precise Lunar
Landing
81st Annual Aerospace Medical Association Scientific Meeting,
Phoenix, AZ, May 9-13, 2010. Sponsored by: ASMA
Young, L.R.; Clark, T.; Stimpson, A.; Duda, K.R.; Oman, C.M.
Sensorimotor Controls and Displays for Safe and Precise Lunar
Landing
61st International Astronautical Congress, Prague, Czech Republic,
September 27-October 1, 2010. Sponsored by: International
Astronautical Federation (IAF)
Zanetti, R.
Multiplicative Residual Approach to Attitude Kalman Filtering
with Unit-Vector Measurements
Space Flight Mechanics Conference, San Diego, CA, February 14-17,
2010. Sponsored by: AAS and AIAA
Zanetti, R.; DeMars, K.J.; Bishop, R.H.
On Underweighting Nonlinear Measurements
Journal of Guidance, Control, and Dynamics, AIAA, Vol. 33, No. 5,
September-October 2010, pp. 1670-1675

Patents Introduction
Patents
Patents
Introduction
Introduction
Draper Laboratory is well known for integrating diverse technical capabilities and technologies into
innovative and creative solutions for problems of national concern. Draper encourages scientists and
engineers to advance the application of science and technology, expand the functions of existing
technologies, and create new ones.
The disclosure of inventions is an important step in documenting these creative efforts and is required under
Laboratory contracts (and by an agreement with Draper that all employees sign). Draper has an established
patent policy and understands the value of patents in directing attention to individual accomplishments.
Pursuing patent protection enables the Laboratory to pursue its strategic mission and to recognize its
employees’ valuable contributions to advancing the state-of-the-art in their technical areas. An issued
patent is also recognition by a critical third party (the U.S. Patent Office) of innovative work for which the
inventor should be justly proud.
On average, Draper’s Patent Committee typically recommends seeking patent protection for 50 percent of
the disclosures received. Millions of U.S. patents have been issued since the first patent in 1836. Through
December 31, 2010, 1,468 Draper patent disclosures have been submitted to the Patent Committee since
1973; 757 of which were approved by Draper’s Patent Committee for further patent action. As of December
31, a total of 552 patents have been granted for inventions made by Draper personnel. Nineteen patents were
issued for calendar year 2010.
THIS YEAR’S FEATURED PATENT IS:
Systems and Methods for High Density
Multi-Component Modules
The following pages present an overview of the technology covered in the patent and the official
patent abstract issued by the U.S. Patent Office.
101

Systems and Methods for High Density
Multi-Component Modules
Scott A. Uhland, Seth M. Davis, Stanley R. Shanfield, Douglas W. White, and Livia M. Racz
U.S. Patent No. 7,727,806; Date Issued: June 1, 2010
Draper’s patented i-UHD technology will enable Draper to take miniaturization to new levels for customers who demand highly capable
systems with minimal size and power requirements. By removing all nonessential elements and stacking layers of components buried in
silicon wafers on top of each other, Draper can fit an entire system into a package the size of a Scrabble tile.
This work is close to transitioning into production for two sponsors, and the extreme miniaturization could be an asset for other customers
in fields ranging from national security to biomedical technology.
Scott A. Uhland is a Member of the Technical Staff at the Palo
Alto Research Center (PARC). Within the Electronic Materials and
Devices Laboratory, Dr. Uhland is developing microfluidic actuated
systems for a variety of commercial applications ranging from
devices for hormone therapy to optical displays. Prior to joining
PARC, he was a Senior Member of the Technical Staff at Draper
Laboratory, where he was the Bioengineering Group Leader and
oversaw the development of a wide variety of technologies and
programs, including biological sensors, tissue engineering, and drug
delivery. He was also a Principal Investigator (PI) at Draper for the
research and development of electronic packaging technologies
that push component densities to the theoretical limit. From
2000 to 2004, he was one of the initial PIs at MicroCHIPS, Inc.,
where he pioneered the use of MEMS technology in the medical
field, particularly in the development of innovative drug delivery
and sensing systems. He has authored more than 35 publications,
reviews, and patents, and holds 60+ pending U.S. applications. Dr.
Uhland received a B.S. in Materials Science and Engineering (summa
cum laude) from Rutgers University, where he served as President of
the Tau Beta Pi Honor Society, and a Ph.D. in Materials Science and
Engineering from MIT.
Seth M. Davis is currently the Associate Director for Communication,
Navigation, and Miniaturization in the Special Programs Office.
He is responsible for business development, strategic planning, and
internal technology investment for first-of-a-kind special communications
systems, miniaturized navigation systems, and advanced
tagging tracking and locating systems. His technical interests focus
on ultra-miniaturization of complex, low-power electronics systems
for sensing, signal processing, and RF communications. Prior to his
current position, he was Division Leader of the Electronics Division.
Mr. Davis received B.S. and M.S. degrees in Electrical Engineering
from MIT and Northeastern University, respectively.
Stanley Shanfield is a Distinguished Member of the Technical
Staff, and has recently been a Technical Director for a variety of
intelligence community programs. He led a team that developed
a miniature, low-power, stable frequency source that maintains
stability to better than 0.1 part-per-billion over several seconds,
suitable for high-performance digital transmitters and receivers.
He also led a team that developed and demonstrated an

Systems and Methods for High Density Multi-Component Modules
103

Left to Right:
Douglas W. White, Stanley R. Shanfield, Livia M. Racz, and Seth M. Davis; missing: Scott A. Uhland
104
Systems and Methods for High Density Multi-Component Modules

Anderson, R.S.; Hanson, D.S.; Kasparian, F.J.; Marinis, T.F.; Soucy, J.W.
Sensor Isolation System
Patent No. 7,679,171, March 16, 2010
Appleby, B.D.; Paradis, R.D.; Szczerba, R.J.
Mission Planning System for Vehicles with Varying Levels of
Autonomy
Patent No. 7,765,038, July 27, 2010
Bernstein, J.J.; Rogomentich, F.J.; Lee, T.W.; Varghese, M.; Kirkos,
G.A. Systems, Methods and Devices for Actuating a Moveable
Miniature Platform
Patent No. 7,643,196, January 5, 2010
Borenstein, J.T.; Weinberg, E.J.; Orrick, B.; Pritchard, E.M.; Barnard, E.;
Krebs, N.J.; Marentis, T.C.; Vacanti, J.P.; Kaazempur-Mofrad, M.R.
Micromachined Bilayer Unit for Filtration of Small Molecules
Patent No. 7,776,021, August 17, 2010
Duwel, A.E.; Varsanik, J.S.
Electromagnetic Composite Metamaterial
Patent No. 7,741,933, June 22, 2010
Elwell Jr., J.M.; Gustafson, D.E.; Dowdle, J.R.
Systems and Methods for Positioning Using Multipath Signals
Patent No. 7,679,561, March 16, 2010
Fiering, J.O.; Varghese, M.
Devices for Producing a Continuously Flowing Concentration
Gradient in Laminar Flow
Patent No. 7,837,379, November 23, 2010
Laine, J-P.J.; Miraglia, P.; Tapalian Jr., H.C.
High Efficiency Fiber-Optic Scintillator Radiation Detector
Patent No. 7,791,046, September 7, 2010
Marinis, T.F.; Kondoleon, C.A.; Pryputniewicz, D.R.
Structures for Crystal Packaging Including Flexible Membranes
Patent No. 7,851,970, December 14, 2010
Mescher, M.J.
High Speed Piezoelectric Optical System with Tunable Focal
Length
Patent No. 7,826,144, November 2, 2010
Sammak, P.J.; Mangoubi, R.S.; Desai, M.N.; Jeffreys, C.G.
Methods and Systems for Imaging Cells
Patent No. 7,711,174, May 4, 2010
List of 2010 Patents
List of 2010 Patents
Sawyer, W.D.
MEMS Devices and Interposer and Method for Integrating MEMS
Device and Interposer
Patent No. 7,655,538, February 2, 2010
Tawney, J.; Hakimi, F.
Methods and Apparatus for Providing a Semiconductor Optical
Flexured Mass Accelerometer
Patent No. 7,808,618, October 5, 2010
Uhland, S.A.; Davis, S.M.; Shanfield, S.R.; White, D.W.; Racz, L.M.
Systems and Methods for High Density Multi-Component
Modules
Patent No. 7,727,806, June 1, 2010
Vacanti, J.P.; Rubin, R.; Cheung, W.; Borenstein, J.T.
Method of Determining Toxicity with Three Dimensional
Structures
Patent No. 7,670,797, March 2, 2010
Vacanti, J.P.; Shin, Y-M.M.; Ogilvie, J.; Sevy, A.; Maemura, T.; Ishii, O.;
Kaazempur-Mofrad, M.R.; Borenstein, J.T.; King, K.R.; Wang, C.C.;
Weinberg, E.J.
Fabrication of Tissue Lamina Using Microfabricated Two-
Dimensional Molds
Patent No. 7,759,113, July 20, 2010
Ward, P.A.
Interferometric Fiber Optic Gyroscope with Off-Frequency
Modulation Signals
Patent No. 7,817,284, October 19, 2010
Weinberg, E.J.; Borenstein, J.T.
Systems, Methods, and Devices Relating to a Cellularized
Nephron Unit
Patent No. 7,790,028, September 7, 2010
Young, J.; Turney, D.J.
Systems and Methods for Reconfigurable Computing
Patent No. 7,669,035, February 23, 2010
105

106
The 2010 Draper Distinguished
Performance Awards
Chairman of the Board John A. Gordon and President Jim Shields presented the 2010 Draper Distinguished
Performance Awards (DPAs) to two teams at the Annual Dinner of the Corporation on October 7. The first
team included Laurent G. Duchesne, Richard D. Elliott, Robert M. Filipek, Sean George, Daniel I. Harjes,
Anthony S. Kourepenis, and Justin E. Vican for the “Design and Demonstration of a Guided Bullet for Extreme
Precision Engagement of Targets at Long Range.” The second team included Stanley R. Shanfield, Albert C.
Imhoff, Thomas A. Langdo, Balasubrahmanyan “Biga” Ganesh, and Peter A. Chiacchi for the “Development
of an Ultra-Miniaturized, Paper-Thin Power Source.”
Each year since 1989, Draper Laboratory presents Distinguished Performance Awards to recognize
extraordinary and unique individual and team performance. A committee of Draper staff representing
every organization evaluated the nominations against the following criteria:
• Constitutes a major technical accomplishment.
• Involves highly challenging and complex tasks of substantial benefit to the Laboratory.
• Is a recent discrete accomplishment that is clearly extraordinary and represents a standard of
excellence for the Laboratory.
• The responsible individual or team can be identified as the prime factor in the results.
• Is regarded as a major advance by the outside community.
The Distinguished Performance Award Evaluation Committee was chaired by Jim Comolli. Committee
members included Mark Abramson, Dick Dramstad, Alex Edsall, Dan Eyring, Al Ferraris, Ryan Prince, Peter
Halatyn, and Livia Racz. Jean Avery provided administrative support.
The 2010 Draper Distinguished Performance Awards

Left to Right:
Daniel I. Harjes, Anthony S. Kourepenis, Sean George, Richard D. Elliott, Laurent G. Duchesne, Justin E. Vican, and Robert M. Filipek
Design and Demonstration of a Guided Bullet for Extreme Precision Engagement of Targets at Long Range
Performing for the DARPA Extreme Accuracy Tasked Ordnance (EXACTO) program, the team developed a revolutionary .50
caliber bullet guidance system that will be used to produce the smallest, fastest, highest g projectile to date that is fully
guided. To perform across a 70,000-g launch acceleration, they designed a first-of-a-kind, two-body bullet with a decoupled
aft section that despins from 120,000 to 0 rpm in under 300 ms. This required the implementation of an innovative, alternator
controlled, despun aft section that provides sufficient maneuverability but low drag for the bullet to remain supersonic out
to maximum range.
The team worked within an 11-month time frame to deliver a system that exceeded all of the accuracy requirements across
a variety of night- and daytime ranges, moving targets, wind speeds and directions, and other environmental conditions.
The effort culminated in May with a physics and experimentally-based, fully integrated hardware- and software-in-the-loop
demonstration that not only validated superior system performance, but also exceeded designated product requirements
over all ranges and all target motion challenges. For this accomplishment, the program was recently awarded Phase II to
continue the design and development of the guidance mechanics and electronics in collaboration with a commercial sponsor.
The outstanding technical achievements demonstrated in the design, fabrication, simulation, and testing of this miniaturized
guidance system are well-deserving of this award.
The 2010 Draper Distinguished Performance Awards
107

108
Back to Front:
Thomas A. Langdo, Albert C. Imhoff, Balasubrahmanyan "Biga" Ganesh, Peter A. Chiacchi, and Stanley R. Shanfield
Development of an Ultra-Miniaturized, Paper-Thin Power Source
This award recognizes a truly revolutionary advance in energy delivery that shows an order of magnitude improvement over
current technologies. The paper-thin-power-source (PTPS) thermoelectric power source has successfully demonstrated a
dramatic breakthrough in miniature portable energy through the combined use of an innovative linear array of miniaturized, heat
scavenging, thermocouple pairs, and extremely efficient dc-dc power converters. These advances will better enable miniature
portable systems to achieve their required mission endurance.
The concept and the fabrication approach both required significant innovation commensurate with the criteria of this award.
PTPS required the thin-film deposition of Bi2Te3 (bismuth telluride) and other high-performance thermoelectric materials that
are difficult to use due to their composition and material defects. The team developed innovative material processing methods
and unique machining and handling procedures to realize the long, thin features necessary for high efficiency and high voltages.
No one had processed this material at these aspect and size ratios before while maintaining its bulk properties. Miniaturization
of the thermocouple pairs was critical to the PTPS design’s success since the combined cross section of the thermocouple pairs
forming the array had to be small enough to prevent conductive heat transfer from reducing the temperature of the source. This
resulted in a prototype that significantly outperformed the current state-of-the-art.
These individuals were the key innovators and implementers who successfully designed, built, and tested this unprecedented,
micron-scale thermoelectric generator system, which led to a successful customer demonstration of the integrated technologies
and the Phase III contract now underway.
The 2010 Draper Distinguished Performance Awards

The 2010 Outstanding Task Leader Awards
Ian T. Mitchell is a Distinguished Member of the Technical Staff in the Dynamic Systems and Control Division. He
has over 25 years of experience in designing and developing GN&C systems for a wide range of space programs.
Prior to joining Draper, he was the lead GN&C engineer for the XSS-10 and XSS-11 microsatellite missions that
successfully demonstrated a number of key technologies related to autonomous rendezvous and proximity
operations. Mr. Mitchell is currently Task Lead for the Commercial Orbital Transportation Services (COTS)
program, which involves the flight demonstration of the Cygnus spacecraft delivering cargo to the International
Space Station (ISS). In this role, he has led Draper’s team in the development of guidance, navigation, and targeting
(GN&T) algorithms and flight software for the Cygnus vehicle. Mr. Mitchell has also provided technical leadership
within Draper in the application of Model-Based Design (MBD) methods to high-assurance, mission-critical GN&C
algorithms and flight software development programs. Mr. Mitchell received a B.Sc. in Mathematics from the
University of Manchester, Institute of Science and Technology (UMIST), UK, and an M.Sc. in Control Engineering
from the City University, London, UK.
Daniel Monopoli is a Principal Member of the Technical Staff and System Integration Group Leader in the System
Integration, Test, and Evaluation Division. Since joining Draper in 2000, he has made significant contributions in
program areas including integration, test, and evaluation of strategic guidance, INS/GPS, and avionics systems.
For the past 5 years, he has held numerous system integration roles within the Navy’s TRIDENT program. He is
currently the Control Account Manager for MARK6 MOD1 System Test Equipment. This is a multiyear, crossdisciplinary
effort between Draper, industrial support contractors, and the Integrated Support Facility to develop,
integrate, test, evaluate, and certify the system test equipment required to perform production acceptance testing
of the MARK6 MOD1 system. Prior to joining Draper, he was a Process Engineer in the Iron, Steel, and Casting
Division for the U.S. Steel Group in Birmingham, Alabama. He is currently a Master’s candidate in Engineering
Management at Tufts Gordon Institute. Mr. Monopoli holds B.E. and M.S. degrees in Mechanical Engineering from
Vanderbilt University.
NEXT PAGE // PHOTOS
The 2010 Outstanding Task Leader Awards
109

Ian Mitchell
2010 Outstanding Task Leader
The 2010 Outstanding Task Leader Awards

Daniel Monopoli
2010 Outstanding Task Leader

112
The 2010 Howard Musoff Student
Mentoring Award
The 2010 Howard Musoff Student Mentoring Award was presented to Sarah L. Tao, a Senior Member of
the Technical Staff in the MEMS Design Group. Sarah received a B.S. in Bioengineering from the University
of Pennsylvania and earned a Ph.D. in Biomedical Engineering from Boston University, where she was also a
NASA Graduate Research Fellow. Before joining Draper, she was a Research Scientist and Sandler Translational
Research Fellow at the University of California, San Francisco. Her research combines fabrication methods
used for MEMS to create therapeutic platforms for cell encapsulation, targeted drug delivery, and templates
for cell and tissue regeneration. She has over 25 publications, and her research efforts in microfabricated
therapeutic platforms have earned numerous accolades, including the Society for Biomaterials Award for
Outstanding Research, the Eurand Grand Prize for Outstanding Novel Research, and the Capsugel/Pfizer
Award for Innovative Research. Away from the Laboratory, Sarah enjoys remaining active. She is a black belt
candidate in Taekwondo, a scuba diver, and with a newfound interest in triathlons, has more recently become
an avid runner and cyclist (swimmer - still to be determined).
Without exception, past recipients of this award have indicated that they have been as enriched by their
mentoring experiences as their students. As Sarah explains it:
“The environment at Draper is unique in that as staff, we are able to engage young minds from major universities
across the greater Boston area through cooperative education programs, senior engineering design courses,
and our own Draper Fellow Program. Likewise, students at Draper are able to capitalize on a diverse network
of experienced staff for support and guidance as they develop an individualized skill set on their pathway
to independent research. This academic year, I had the benefit of working with three exceptional graduate
students from MIT (Mechanical Engineering) and Boston University (Biomedical Engineering and Medical
Sciences)—each with different backgrounds, interests, and personal goals. However, what they share in
common is a remarkable motivation: an eagerness to learn, grow, and take ownership of their research. I believe
the student-mentor partnership at Draper is both collaborative and reciprocal in every way. We work together
to define goals—both research and career-wise—to work toward. And as the students inevitably come across
problems, collectively, we look at their research from all angles and discuss options and strategies to maximize
success. These talented and creative students continuously bring their unique skills, perspectives, and ideas
to our research on a daily basis. Their drive and determination has been critical in the successful execution of
multiple research projects, manuscript submissions, and conference presentations. And their input has been
central in generating new lines of collaboration with our existing academic partners. It has been a privilege to
work with and learn from each of my students this year.”
The Howard Musoff Student Mentoring Award was established in his memory in 2005. A Draper employee
for over 40 years, Musoff advised and mentored many Draper Fellows. The award is presented each February
during National Engineers Week to recognize staff members who, like Musoff, share their expertise and
supervise the professional development and research activities of Draper Fellows. The award, endowed by
the Howard Musoff Charitable Foundation, includes a $1,000 honorarium and a plaque. Each Engineering
Division Leader may submit one nomination of a staff person from his Division. The Education Office assists in
the process by soliciting comments from students who were residents during that time period. The Selection
Committee consists of the Vice President of Engineering, the Principal Director of Engineering, and the Director
of Education.
The 2010 Howard Musoff Student Mentoring Award

Sarah L. Tao

114
The 2010 Excellence in Innovation Award
Catherin L. Slesnick, Benjamin F. Lane, Donald E. Gustafson, and Brad D. Gaynor received the 2010 Excellence in
Innovation Award for their work on Navigation by Pressure. Under government sponsorship, Draper is developing
technology to track objects at sea in a completely RF-denied environment. Geolocation is performed using
barometric pressure. A small pressure sensor logs measurements of ambient barometric pressure and time.
The recorded pressure time series is then correlated against gridded, high-quality, worldwide, and regional
pressure data sets available from the weather monitoring community.
Catherine L. Slesnick is a Senior Member of the Technical Staff at Draper Laboratory. Her professional
interests include working with large datasets from remote sensing and ground-based observing platforms.
Emphasis has been on signal processing, time series analysis, fusion of information from heterogeneous
sensors, and sensor system simulation. She has been involved with multiple projects involving analysis of Earth
science and meteorological observations and/or astronomical observations. She is currently Technical Lead
for the Navigation by Pressure project and Lead Photometric Scientist for the U.S. Naval Observatory (USNO)sponsored
Joint Milli-Arcsecond Pathfinder Survey (JMAPS) satellite mission. Before joining Draper, she was a
Fellow at the Department of Terrestrial Magnetism, Carnegie Institution for Science in Washington DC. She has
30+ publications in the combined fields of astrophysics and Earth science data analysis. Dr. Slesnick earned
a B.A. in Physics and Mathematics from New York University and a Ph.D. in Astrophysics from the California
Institute of Technology as a National Science Foundation Graduate Fellow.
Benjamin F. Lane is a Senior Member of the Technical Staff at Draper Laboratory and is currently the Task
Lead for the Guidance System Concepts effort. Expertise includes development of advanced algorithms for
image processing and real-time control systems; developing instrument concepts, requirements, designs,
control software, integration, testing and commissioning, and operations, debugging, data acquisition. He
helped design, build, and operate a multiple-aperture telescope system (the Palomar Testbed Interferometer)
for extremely high-angular resolution (picorad) astronomical observations, and also designed and built highcontrast
imaging payloads for sounding rocket missions and spacecraft. He has published more than 45 peerreviewed
papers in his area of expertise. Dr. Lane holds a Ph.D. in Planetary Science from the California Institute
of Technology.
Donald E. Gustafson is a Distinguished Member of the Technical Staff at Draper Laboratory and has over 40
years of experience in conceptual design, analysis and simulation of complex systems. Expertise includes
development of advanced algorithms for GPS-based navigation, multipath exploitation in indoor and urban
environments, robotic localization and tracking, underground object detection using ground penetrating radar,
space/time adaptive signal processing, and biomedical signal processing and pattern recognition. He was one
of the principal developers of Draper’s patented Deep Integration system, a nonlinear filtering algorithm for
GPS-based code and carrier tracking. More recently, he has worked on inversion of GPS measurements for
atmospheric refractivity tomography and has developed algorithms for navigation using atmospheric pressure
measurements. Draper awards he has received include two Best Publication awards, two Patent of the Year
awards, and co-recipient of the 2000 Distinguished Performance Award. He has also received two Best Paper
awards from the Institute of Navigation. Dr. Gustafson has published more than 40 papers and holds a Ph.D. in
Instrumentation and Control from MIT.
Brad D. Gaynor is a Program Manager in the Special Operations Program Office at Draper Laboratory. He
manages a number of programs that utilize key Draper technologies, including deep-fade GPS processing;
miniature, low-power hardware, and multisensor navigation. Mr. Gaynor is currently enrolled in a Ph.D. program
at Tufts University, where he also earned B.S. and M.S. degrees.
The 2010 Excellence in Innovation Award

Benjamin F. Lane, Catherine L. Slesnick, Donald E. Gustafson, and Brad D. Gaynor
115

116
List of 2010 Graduate Research Theses
During 2010, over 50 students pursued their graduate degree programs while participating in the Draper Fellows
program, conducting research in a wide variety of topics at top universities, including MIT, northeastern University,
and Rice University. Theses that were completed in 2010 are listed below. Theses that were completed in 2009 after
the Digest went to press are also listed. Details on these and other student research can be obtained by contacting the
Draper Education Office at ed@draper.com.
Burke, D.; Supervisors: Spanos, P.; Dick, A.; Meade, A.J.; Bedrossian,
N.; King, E.
On-Orbit Transfer Trajectory Methods Using High Fidelity
Dynamic Models
Master of Science Thesis, Rice University, April 2010
Clark, T.; Supervisors: Young, L.R.; Duda, K.R.; Modiano, E.
Human Spatial Orientation Perceptions During Simulated
Lunar Landing
Master of Science Thesis, MIT, June 2010
Deshmane, A.V.; Supervisors: Mark, R.G.; Kessler, L.J.; Terman, C.J.
False Arrhythmia Alarm Suppression Using ECG, ABP, and
Photoplethysmogram
Master of Engineering Thesis, MIT, August 2009
Herold, T.M.; Supervisors: Abramson, M.; Balakrishnan, H.; Bertsimas, D.
Asynchronous, Distributed Optimization for the Coordinated
Planning of Air and Space Assets
Master of Science Thesis, MIT, June 2010
Hung, B.W.; Supervisors: Kolitz, S.E.; Ozdaglar, A.; Bertsimas, D.
Optimization-Based Selection of Influential Agents in a Rural
Afghan Social Network
Master of Science Thesis, MIT, June 2010
Jeon, J.; Supervisors: Charest, J.; Kamm, R.D.; Hardt, D.E.
3D Cyclic Olefin Copolymer (COC) Microfluidic Chip
Fabrication Using Hot Embossing Method for Cell Culture
Platform
Master of Science Thesis, MIT, June 2010
Kotru, K.; Supervisors: Ezekiel, S.; Stoner, R.E.; Modiano, E.
Toward a Demonstration of a Light Force Accelerometer
Master of Science Thesis, MIT, September 2010
Marrero, J.; Supervisors: Cleary, M.E.; Katz, B.; Terman, C.J.
Resolution of Linear Entity and Path Geometries Expressed via
Partially-Geospatial Natural Language
Master of Engineering Thesis, MIT, February 2010
Middleton, A.J.; Supervisors: Hoffman, J.; Paschall II, S.C.; Modiano, E.
Modeling and Vehicle Performance Analysis of Earth and Lunar
Hoppers
Master of Science Thesis, MIT, September 2010
Owen, R.; Supervisors: Hansman, R.J.; Kessler, L.J.; Modiano, E.
Modeling the Effect of Trend Information on Human Failure
Detection and Diagnosis in Spacecraft Systems
Master of Science Thesis, MIT, June 2010
List of 2010 Graduate Research Theses
Richards, J.E.; Supervisors: Major, L.M.; Rhodes, D.; Hale, P.
Integrating the Army Geospatial Enterprise: Synchronizing
Geospatial-Intelligence to the Dismounted Soldier
Master of Science Thesis, MIT, June 2010
Savoie, T.B.; Supervisors: Frey, D.D.; McCarragher, B.C.; Hardt, D.E.
Human Detection of Computer Simulation Mistakes in
Engineering Experiments
Doctor of Philosophy Thesis, MIT, June 2010
Seidel, S.B.; Supervisors: Hildebrant, R.R.; Graves, S.C.; Bertsimas, D.
Planning Combat Outposts to Maximize Population Security
Master of Science Thesis, MIT, June 2010
Shenk, K.N.; Supervisors: Markuzon, N.; Bertsimas, D.; Jaillet, P.
Patterns of Heart Attacks
Master of Science Thesis, MIT, June 2010
Sievers, A.; Supervisors: Spanos, P.D.; Dick, A.; Meade, A.J.; Zanetti, R.;
D’Souza, C.
Multiple Event Triggers in Linear Covariance Analysis for
Orbital Rendezvous
Master of Science Thesis, Rice University, April 2010
Small, T.; Supervisors: Hall, S.R.; Proulx, R.J.; Modiano, E.
Optimal Trajectory-Shaping with Sensitivity and Covariance
Techniques
Master of Science Thesis, MIT, May 2010
Snyder, A.M.; Supervisors: Markuzon, N.; Welsch, R.; Bertsimas, D.
Data Mining and Visualization: Real Time Predictions
and Pattern Discovery in Hospital Emergency Rooms and
Immigration Data
Master of Science Thesis, MIT, June 2010
Wilder, J.; Supervisors: Spanos, P.D.; Jang, J.-W.; Meade, A.J.;
Stanciulescu, I.
Time-Varying Stability Analysis of Linear Systems with Linear
Matrix Inequalities
Master of Science Thesis, Rice University, May 2010
Xu, Y.; Supervisors: Madison, R.W.; Poggi, T.A.; Terman, C.J.
VICTORIOUS: Video Indexing with Combined Tracking and
Object Recognition for Improved Object Understanding in
Scenes.
Master of Engineering Thesis, MIT, July 2009

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