Set JPDA Filter for Multitarget Tracking
Artikel i vetenskaplig tidskrift, 2011

In this article, we show that when targets are closely spaced, traditional tracking algorithms can be adjusted to perform better under a performance measure that disregards identity. More specifically, we propose an adjusted version of the joint probabilistic data association (JPDA) filter, which we call set JPDA (SJPDA). Through examples and theory we motivate the new approach, and show its possibilities. To decrease the computational requirements, we further show that the SJPDA filter can be formulated as a continuous optimization problem which is fairly easy to handle. Optimal approximations are also discussed, and an algorithm, Kullback–Leibler SJPDA (KLSJPDA), which provides optimal Gaussian approximations in the Kullback–Leibler sense is derived. Finally, we evaluate the SJPDA filter on two scenarios with closely spaced targets, and compare the performance in terms of the mean optimal subpattern assignment (MOSPA) measure with the JPDA filter, and also with the Gaussian-mixture cardinalized probability hypothesis density (GM-CPHD) filter. The results show that the SJPDA filter performs substantially better than the JPDA filter, and almost as well as the more complex GM-CPHD filter.

Bayes methods

filtering theory

random finite set theory

recursive estimation

target tracking.


Lennart Svensson

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

Daniel Svensson

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

Marco Guerriero

Far Systems S.P.A.

P. Willett

University of Connecticut

IEEE Transactions on Signal Processing

1053-587X (ISSN) 1941-0476 (eISSN)

Vol. 59 10 4677-4691 5942195


Informations- och kommunikationsteknik



Sannolikhetsteori och statistik


Annan elektroteknik och elektronik



Mer information