Set JPDA Filter for Multi-Target Tracking
Report, 2010

In this report 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, 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 Gaussianmixture 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.

filtering theory

random finite set theory

Target tracking

recursive estimation

Bayes methods.

Author

Lennart Svensson

Chalmers, Signals and Systems

Daniel Svensson

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

Marco Guerriero

University of Connecticut

Subject Categories

Signal Processing

Other Electrical Engineering, Electronic Engineering, Information Engineering

R - Department of Signals and Systems, Chalmers University of Technology: R012

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