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Proceedings Paper

The Set IMMJPDA filter for multitarget tracking
Author(s): Daniel Svensson; David F. Crouse; Lennart Svensson; Marco Guerriero; Peter Willett
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Paper Abstract

The Set JPDA (SJPDA) filter is a recently developed multi-target tracking filter that utilizes the relation between the density of a random finite set and the ordinary density of a state vector to improve on the Joint Probabilistic Data Association (JPDA) filter. One advantage to the filter is the improved accuracy of the Gaussian approximations of the JPDA, which results in avoidance of track coalescence. Another advantage is an improved estimation accuracy in terms of a measure which disregards target identity. In this paper we extend the filter to also consider multiple motion models. As a basis for the extension we use the Interacting Multiple Model (IMM) algorithm. We derive three alternative filters that we jointly refer to as Set IMMJPDA (SIMMJPDA). They are based on two alternative descriptions of the IMMJPDA filter. In the paper, we also present simulation results for a two-target tracking scenario, which show improved tracking performance for the Set IMMJPDA filter when evaluated with a measure that disregards target identity.

Paper Details

Date Published: 5 May 2011
PDF: 12 pages
Proc. SPIE 8050, Signal Processing, Sensor Fusion, and Target Recognition XX, 80500O (5 May 2011); doi: 10.1117/12.886937
Show Author Affiliations
Daniel Svensson, Chalmers Univ. of Technology (Sweden)
David F. Crouse, Univ. of Connecticut (United States)
Lennart Svensson, Chalmers Univ. of Technology (Sweden)
Marco Guerriero, Elt Elettronica S.p.A. (Italy)
Peter Willett, Univ. of Connecticut (United States)


Published in SPIE Proceedings Vol. 8050:
Signal Processing, Sensor Fusion, and Target Recognition XX
Ivan Kadar, Editor(s)

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