Set JPDA Filter for Multi-Target Tracking
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
random finite set theory