Optimal PHD filter for single-target detection and tracking

Ronald Maher

doi:10.1117/12.735629

21 September 2007 Optimal PHD filter for single-target detection and tracking

Ronald Maher

Proceedings Volume 6699, Signal and Data Processing of Small Targets 2007; 669913 (2007) https://doi.org/10.1117/12.735629
Event: Optical Engineering + Applications, 2007, San Diego, California, United States

Abstract

The PHD filter has attracted much international interest since its introduction in 2000. It is based on two approximations. First, it is a first-order approximation of the multitarget Bayes filter. Second, to achieve closed-form formulas for the Bayes data-update step, the predicted multitarget probability distribution must be assumed Poisson. In this paper we show how to derive an optimal PHD (OPHD) filter, given that target number does not exceed one. (That is, we restrict ourselves to the single-target detection and tracking problem.) We further show that, assuming no more than a single target, the following are identical: (1) the multitarget Bayes filter; (2) the OPHD filter; (3) the CPHD filter; and (4) the multi-hypothesis correlation (MHC) filter. We also note that all of these are generalizations of the probabilistic data association (IPDA) filter of Musicki, Evans, and Stankovic.

Citation Download Citation

Ronald Maher "Optimal PHD filter for single-target detection and tracking", Proc. SPIE 6699, Signal and Data Processing of Small Targets 2007, 669913 (21 September 2007); https://doi.org/10.1117/12.735629

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