Set JPDA Filter for Multitarget Tracking
Journal article, 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.

Author

Lennart Svensson

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Daniel Svensson

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Marco Guerriero

Far Systems S.P.A.

P. Willett

University of Connecticut

IEEE Transactions on Signal Processing

1053-587X (ISSN)

Vol. 59 10 4677-4691 5942195

Areas of Advance

Information and Communication Technology

Transport

Subject Categories

Probability Theory and Statistics

Signal Processing

Other Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1109/TSP.2011.2161294

More information

Created

10/7/2017