Gaussian mixtures in multi-target tracking: a look at Gaussian mixture probability hypothesis density and integrated track splitting

The multi-target tracking in clutter, assuming linear target trajectory propagation and linear target measurement equation, naturally leads to a Gaussian mixture (GM) target tracking solution. This study examines and compares the two prominent methods that use the GMs: the probability hypothesis den...

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Bibliographic Details
Published inIET radar, sonar & navigation Vol. 6; no. 5; p. 359
Main Authors Song, T L, Musicki, D, Kim, D S, Radosavljevic, Z
Format Journal Article
LanguageEnglish
Published Stevenage The Institution of Engineering & Technology 01.06.2012
Subjects
Algorithms
Density
Gaussian
Mathematical analysis
Mathematical models
Sonar
Splitting
Tracking
Trajectories
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Summary:The multi-target tracking in clutter, assuming linear target trajectory propagation and linear target measurement equation, naturally leads to a Gaussian mixture (GM) target tracking solution. This study examines and compares the two prominent methods that use the GMs: the probability hypothesis density and the integrated track splitting. Both are recursive Bayes methods and both incorporate the false track discrimination capabilities. They are represented in the form of GM target density filters. The modelling assumptions are translated in the algorithmic requirements. The authors compare the algorithms on the basis of these requirements with the future work indicated to reconcile algorithms and requirements.
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ISSN:1751-8784
1751-8792
DOI:10.1049/iet-rsn.2011.0263