Distributed Tracking with a PHD Filter using Efficient Measurement Encoding

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1 December 2012
B. K. Habtemariam, A. Aravinthan, R. Tharmarasa, K. Punithakumar, T. Lang, T. Kirubarajan

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Probability Hypothesis Density (PHD) filter is a framework for multitarget tracking, which provides estimates for the number of targets as well as the individual target states. Sequential Monte Carlo (SMC) implementation of a PHD filter can be used for non-linear 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 efficiently communicates encoded measurements between nodes while maintaining the filter accuracy is proposed. This coding is com-plicated 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 Cramer´-Rao Lower Bound (PCRLB) that incorporates quantization errors. Simulation studies are performed to demonstrate the effectiveness of the proposed algorithm.