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Proceedings Paper

A fast coalescence-avoiding JPDAF
Author(s): Kevin Romeo; David F. Crouse; Yaakov Bar-Shalom; Peter Willett
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Paper Abstract

In this paper we present a new algorithm for approximating the target-measurement association probabilities of the Joint Probabilistic Data Association Filter (JPDAF). This algorithm is designed to robustify the JPDAF against track coalescence which can greatly degrade the performance of the JPDAF and other approximate algorithms. It is based on the works of Roecker and the JPDAF* of Blom and Bloem. We compare our new algorithm with the two it is based on, as well as the "cheap JPDAF" and the Set JPDAF, and show that it offers a significant improvement in computational complexity over the JPDAF*, and improvement in tracking error over the Roecker algorithm. We compare their performance with respect to the Mean Optimal Subpattern Assignment (MOSPA) statistic in scenarios involving several closely-spaced targets. A consistency comparison of the various algorithms considered is also presented.

Paper Details

Date Published: 15 May 2012
PDF: 14 pages
Proc. SPIE 8393, Signal and Data Processing of Small Targets 2012, 83930U (15 May 2012); doi: 10.1117/12.924335
Show Author Affiliations
Kevin Romeo, Univ. of Connecticut (United States)
David F. Crouse, Univ. of Connecticut (United States)
Yaakov Bar-Shalom, Univ. of Connecticut (United States)
Peter Willett, Univ. of Connecticut (United States)

Published in SPIE Proceedings Vol. 8393:
Signal and Data Processing of Small Targets 2012
Oliver E. Drummond; Richard D. Teichgraeber, Editor(s)

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