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Proceedings Paper

Distributed tracking with probability hypothesis density filters using efficient measurement encoding
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Paper Abstract

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.

Paper Details

Date Published: 3 September 2009
PDF: 12 pages
Proc. SPIE 7445, Signal and Data Processing of Small Targets 2009, 74450H (3 September 2009); doi: 10.1117/12.826552
Show Author Affiliations
A. Aravinthan, McMaster Univ. (Canada)
R. Tharmarasa, McMaster Univ. (Canada)
Tom Lang, General Dynamics Canada Ltd. (Canada)
Mike McDonald, Defence Research and Development Canada (Canada)
T. Kirubarajan, McMaster Univ. (Canada)


Published in SPIE Proceedings Vol. 7445:
Signal and Data Processing of Small Targets 2009
Oliver E. Drummond; Richard D. Teichgraeber, Editor(s)

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