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Proceedings Paper

A physical-space approach for the probability hypothesis density and cardinalized probability hypothesis density filters
Author(s): Ozgur Erdinc; Peter Willett; Yaakov Bar-Shalom
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Paper Abstract

The probability hypothesis density (PHD) filter, an automatically track-managed multi-target tracker, is attracting increasing but cautious attention. Its derivation is elegant and mathematical, and thus of course many engineers fear it; perhaps that is currently limiting the number of researchers working on the subject. In this paper, we explore a physical-space approach - a bin model - which leads us to arrive the same filter equations as the PHD. Unlike the original derivation of the PHD filter, the concepts used are the familiar ones of conditional probability. The original PHD suffers from a "target-death" problem in which even a single missed detection can lead to the apparent disappearance of a target. To obviate this, PHD originator Mahler has recently developed a new "cardinalized" version of PHD (CPHD). We are able to extend our physical-space derivation to the CPHD case as well. We stress that the original derivations are mathematically correct, and need no embellishment from us; our contribution here is to offer an alternative derivation, one that we find appealing.

Paper Details

Date Published: 19 May 2006
PDF: 12 pages
Proc. SPIE 6236, Signal and Data Processing of Small Targets 2006, 623619 (19 May 2006); doi: 10.1117/12.673194
Show Author Affiliations
Ozgur Erdinc, Univ. of Connecticut (United States)
Peter Willett, Univ. of Connecticut (United States)
Yaakov Bar-Shalom, Univ. of Connecticut (United States)

Published in SPIE Proceedings Vol. 6236:
Signal and Data Processing of Small Targets 2006
Oliver E. Drummond, Editor(s)

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