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Proceedings Paper

A comparison of "clutter-agnostic" PHD filters
Author(s): Ronald Mahler
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Paper Abstract

This paper describes a general approach for deriving PHD/CPHD filters that must estimate the background clutter process, rather than being provided with it a priori. I first derive general time- and measurementupdate equations for clutter-agnostic PHD filters. I then consider two different Markov motion models. For the Uncoupled Motion (UM) model, targets can transition only to targets, and clutter generators can transition only to clutter generators. For the Coupled Motion (CM) model, targets can transition to clutter generators and vice-versa. I demonstrate that R. Streit's "multitarget intensity filter" (MIF) is actually a PHD filter with a CM model. Streit has made the following claims for the MIF: it subsumes the conventional PHD filter as a special case, and can estimate both the clutter rate λk+1 and the target-birth rate Bk+1|k. I exhibit counterexamples to these claims. Because of the CM model, the MIF (1) does not subsume the conventional PHD filter as a special case; (2) cannot estimate Bk+1|k when there are no clutter generators; and (3) cannot estimate λk+1 when the target birth-rate and target death-rate are "conjugate." By way of contrast, PHD filters with UM models do include the PHD filter as a special case, and can estimate the clutter intensity function κk+1(z). I also show that the MIF is essentially identical to the UM-model PHD filter when the target birth-rate and death-rate are both small.

Paper Details

Date Published: 17 May 2012
PDF: 12 pages
Proc. SPIE 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI, 83920L (17 May 2012); doi: 10.1117/12.920799
Show Author Affiliations
Ronald Mahler, Lockheed Martin MS2 (United States)


Published in SPIE Proceedings Vol. 8392:
Signal Processing, Sensor Fusion, and Target Recognition XXI
Ivan Kadar, Editor(s)

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