Distributed Tracking with Probability Hypothesis Density Filters Using Efficient Measurement Encoding

Abstract

<p>Probability Hypothesis Density (PHD) filter is a unified framework for multitarget tracking that 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 thesis, 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 adaptive quantization and encoding techniques. The performance of the algorithm is quantified using a Posterior Cramér-Rao Lower Bound (PCRLB), which incorporates quantization errors. Simulation studies are performed to demonstrate the effectiveness of the proposed algorithm.</p>