FRIDAY MORNING, 20 MAY 2005 REGENCY E, 8:30 A.M. TO 12:00 ...

10:05–10:20 Break
Contributed Papers
10:20
5aSP4. Acoustic self-localization of a wireless sensor network. Peter
L. Schmidt, Stephen M. Williams, and Kenneth D. Frampton Vanderbilt
Univ., 2400 Highland Ave., Nashville, TN 37212
One of the biggest challenges to the field of wireless sensor networks
is self-localization: that is the determination of the relative and absolute
coordinates of each sensor node in the network. Previous work has been
done to locate hydrophone arrays. However sensor networks have some
unique constraints that make this more challenging. A typical application
would involve the distribution of hundreds or thousands of sensor nodes
over an area either by hand, airdrop or other means. One of the primary
constraints on such a system is that centralized processing of selflocalization
data may be prohibitively complex. Furthermore, the data may
be incomplete, contain reflected path events, and may be subject to other
mission specific constraints. Therefore, a distributed computational
scheme has been developed to solve acoustic time-of-arrival equations. A
priori information about some sensor locations and user triggered source
localization events are used along with a regularized inversion solution.
Results of this will be presented based on both small scale experiments
and larger systems simulations. Limits of the types of a priori information
required for accurate results are detailed, along with studies of the accuracies
obtained using various distributed calculations parameters.
10:35
5aSP5. Distributed source localization in a wireless sensor network.
Stephen M. Williams, Peter L. Schmidt, and Kenneth D. Frampton
Vanderbilt Univ., 2400 Highland Ave., Nashville, TN 37212
This work concerns experimental implementation of distributed acoustic
source localization with a wireless sensor network. The envisioned
application is the distribution of hundreds or thousands of small, semidisposable,
wireless sensor nodes possibly by airdrop. These nodes
would form an ad-hoc communications network and monitor and track
acoustic events within the array. Centralized processing of such data
would be prohibited by the excessive communications and complexity of
calculations. Furthermore, localization with a network of randomly placed
sensors is not suited to use of traditional array theories due to irregular
placement of sensors and the unknown sensitivity and phase relationships
between sensors. Therefore, a fully distributed localization algorithm was
developed in which nodes organize themselves into groups which collaborate
to locate sources. Acoustic event time-of-arrival information is shared
among group members and the source locations were determined using
Tikhonov regularized inversion. Small scale localization experiments
with 10 sensor nodes were conducted to validate the performance of the
method. Results are discussed, along with limitations of the method discovered
during conduct of the experiments. In addition, a comparison is
made between this method and traditional matrix solution methods.
10:50
5aSP6. Particle filters for tracking a vehicle through a field of acoustic
directional frequency and recording „DIFAR… sensors. Anton J. Haug
The MITRE Corp., 7515 Colshire Dr., McLean, VA 22102,
ahaug@mitre.org
For nonlinear/non-Gaussian processes, Bayesian approaches to tracking
require integration over probability density functions, which cannot be
accomplished in a closed form. Recently, numerical Monte Carlo ‘‘particle
filter’’ integration techniques have been developed and applied to tracking
problems. However, multidimensional application examples of these techniques
have been limited because they require an analytic expression for
the likelihood function, which is usually not easy to obtain when the noise
is non-Gaussian and not additive. Tracking a vehicle through a field of
acoustic DIFAR sensors is such an application. Although the vehicle dynamic
model can be linear with additive Gaussian noise, the observation
model is inherently nonlinear with observations that contain embedded
non-Gaussian noise. For the DIFAR vehicle tracking problem, we will
consider large time-bandwidth signals, develop both the dynamic and observation
models, and show how the Gaussian additive noise on each
DIFAR channel results in non-Gaussian noise embedded in the bearing
observations. The likelihood function will then be presented and the performance
of several particle filter trackers will be compared with that of an
Extended Kalman Filter.
11:05
5aSP7. Source localization in complex scattering environments using
random amplitude and bearing information. D. Keith Wilson, Mark
L. Moran, and Roy Greenfield USACE ERDC-CRREL, 72 Lyme Rd.,
Hanover, NH 03755-1290, D.Keith.Wilson@erdc.usace.army.mil
By scattering sound waves, atmospheric turbulence and other outdoor
objects such as building and trees induce random fading and angle-ofarrival
variations in signals received by small-baseline microphone arrays.
This study addresses the relative utility of signal amplitude and bearing
estimates when such variations are present. Source-localization simulations
are performed for four idealized statistical scattering models: no
scattering, weak turbulent scattering, strong turbulent scattering, and diffuse
multiple scattering. Each of these cases is considered with low,
moderate, and high SNR. It is found that bearing information provides
highly accurate source localization when SNR is high and scattering is
negligible. However, when the SNR is low and/or there is significant scattering,
the bearing information loses its utility while the amplitude information
remains relatively robust. Algorithms are considered that attempt
to assess the relative reliability of the bearing and amplitude information,
and subsequently weight signal features to provide the most satisfactory
results. The simulations also confirm previous analyses suggesting that, for
unfavorable propagation conditions and SNR, Cramer-Rao lower bounds
predict substantially better performance than is obtainable in practice.
11:20
5aSP8. A method for locating nearby sources. John V. Olson, Kenneth
Arnoult, and Curt. A. S. Szuberla Geophysical Inst., Univ. of Alaska,
Fairbanks, AK 99775
A fast method for locating nearby sources based solely on the time-offlight
information is described. The time delays for a signal passing between
all sensor pairs is determined using cross-correlations and a vector
of time delays is constructed. In the noise free case, where the speed of
sound is constant, each point in the plane is associated with a unique value
of the time delay vector. The method we present uses a fast, simplex-based
search of the plane for a source location by minimizing the difference
between the vector associated with the candidate location on the plane and
the value estimated from cross-correlations. The search takes place over
the three dimensional space that includes two coordinates in the plane and
the propagation speed. The starting point for the search is constructed
from an analytic fit of circular wave fronts to groups of sensors within the
array. This method has been useful in identifying near-field sources in the
153US and 155US CTBT/IMS infrasound arrays. Examples of the locations
we have determined along with a description of the statistical confidence
limits associated with the method will be presented.
2610 J. Acoust. Soc. Am., Vol. 117, No. 4, Pt. 2, April 2005 149th Meeting: Acoustical Society of America 2610