Probability Hypothesis Density (PHD) filter is a unified framework for multitarget tracking and provides estimates
for a number of targets as well as individual target states. Sequential Monte Carlo (SMC) implementation
of a PHD filter can be used for nonlinear non-Gaussian problems. However, the application of PHD based state
estimators for a distributed sensor network, where each tracking node runs its own PHD based state estimator,
is more challenging compared with single sensor tracking due to communication limitations. A distributed state
estimator should use the available communication resources efficiently in order to avoid the degradation of filter
performance. In this paper, a method that communicates encoded measurements between nodes efficiently while
maintaining the filter accuracy is proposed. This coding is complicated in the presence of high clutter and
instantaneous target births. This problem is mitigated using novel adaptive quantization and encoding techniques.
The performance of the algorithm is quantified using a Posterior Cramer-Rao Lower Bound (PCRLB),
which incorporates quantization errors. Simulation studies are performed to demonstrate the effectiveness of the
proposed algorithm.
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