Military Communications and Information Technology: A Trusted ...

290 Military Communications and Information Technology...
• Distributed Kalman Filter using Relaxed Evolution (DKF-RE): The relaxed
evolution model describes the transition kernel with an increased process
noise covariance by a factor of S. The prediction is therefore given by
s
s
kk | 1
,
kk | 1 k1| k1 kk | 1 S
kk | 1 P F P F Q
(29)
s
s
x .
kk | 1 Fkk | 1xk1| k1 (30)
This approach spares out the decorrelation by remote covariance matrices.
Instead, an approximation which relies only on local parameters and the constant
number of sensors is used.
x 10 4
Target state estimates
3
2
1
Y
0
−1
−2
−3
−3 −2 −1 0 1 2 3
X
x 10 4
Figure 4. Example trajectory of the simulated target in the field of view of 20 sensors
V. Evaluation
In this section the previous fusion schemes are assessed through simulation.
In a single simulation run each algorithm uses the same measurement data.
The trajectory is sampled accordingly to the Discrete White Noise Acceleration Model
(DWNAM) [1] and all filters use perfectly matched models for the dynamics and
the sensors, respectively. A Gaussian distributed zero-mean measurement noise
is simulated for each sensor measuring the position of a single target with a variance
of 500 m 2 in x- and y-direction. Figure 4 shows an example trajectory of the target.
Packet losses by network effects such as congestion and buffer overflows or
unreliable Layer-2-Links are simulated by randomly discarding data, which is sent
over the connecting network. Figure 5 (a) – (d) shows the Root Mean Squared Er-