Joint probabilistic data association methods avoiding track coalescence

For the problem of tracking multiple targets the joint probabilistic data association (JPDA) filter has shown to be very effective in handling clutter and missed detections. The JPDA, however, also tends to coalesce neighbouring tracks. Through comparing JPDA with the exact nearest neighbour PDA (EN...

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Bibliographic Details
Published inProceedings of 1995 34th IEEE Conference on Decision and Control Vol. 3; pp. 2752 - 2757 vol.3
Main Authors Bloem, E.A., Blom, H.A.P.
Format Conference Proceeding
LanguageEnglish
Published IEEE Control Systems Society 1995
Subjects
Approximation algorithms
Bayesian methods
Electrical resistance measurement
Equations
Filters
Gaussian approximation
Stochastic processes
Stochastic systems
Target tracking
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ISBN0780326857
9780780326859
ISSN0191-2216
DOI10.1109/CDC.1995.478532

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Summary:For the problem of tracking multiple targets the joint probabilistic data association (JPDA) filter has shown to be very effective in handling clutter and missed detections. The JPDA, however, also tends to coalesce neighbouring tracks. Through comparing JPDA with the exact nearest neighbour PDA (ENNPDA) filter, Fitzgerald has shown that hypotheses pruning is an effective way to prevent track coalescence. The dramatic pruning used for ENNPDA however leads to an undesired sensitivity to clutter and missed detections. In this paper new algorithms are derived which combine the advantages of JPDA and ENNPDA. The effectiveness of the new algorithms is shown through Monte Carlo simulations.
ISBN:0780326857
9780780326859
ISSN:0191-2216
DOI:10.1109/CDC.1995.478532