A theory of PHD filters of higher order in target number

Ronald Mahler

doi:10.1117/12.667083

17 May 2006 A theory of PHD filters of higher order in target number

Ronald Mahler

Proceedings Volume 6235, Signal Processing, Sensor Fusion, and Target Recognition XV; 62350K (2006) https://doi.org/10.1117/12.667083
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

Abstract

The multitarget recursive Bayes nonlinear filter is the theoretically optimal approach to multisensor-multitarget detection, tracking, and identification. For applications in which this filter is appropriate, it is likely to be tractable for only a small number of targets. In earlier papers we derived closed-form equations for an approximation of this filter based on propagation of a first-order multitarget moment called the probability hypothesis density (PHD). In a recent paper, Erdinc, Willett, and Bar-Shalom argued for the need for a PHD-type filter which remains first-order in the states of individual targets, but which is higher-order in target number. In this paper we show that this and much more is possible. We derive a closed-form cardinalized PHD (CPHD), filter, which propagates not only the PHD but also the entire probability distribution on target number.

Citation Download Citation

Ronald Mahler "A theory of PHD filters of higher order in target number", Proc. SPIE 6235, Signal Processing, Sensor Fusion, and Target Recognition XV, 62350K (17 May 2006); https://doi.org/10.1117/12.667083

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