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Mixture reduction techniques and probabilistic intensity models for multiple hypothesis tracking of targets in clutter

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dc.contributor.authorKennedy, H.L.
dc.date.issued2013
dc.description.abstractA linear combination of Gaussian components, i.e. a Gaussian ‘mixture’, is used to represent the target probability density function (pdf) in Multiple Hypothesis Tracking (MHT) systems. The complexity of MHT is typically managed by ‘reducing’ the number of mixture components. Two complementary MHT mixture reduction algorithms are proposed and assessed using a simulation involving a cluttered infrared (IR) seeker scene. A simple means of incorporating intensity information is also derived and used by both methods to provide well balanced peak-to-track association weights. The first algorithm (MHT-2) uses the Integral Squared Error (ISE) criterion, evaluated over the entire posterior MHT pdf, in a guided optimization procedure, to quickly fit at most two components. The second algorithm (MHT-PE) uses many more components and a simple strategy, involving Pruning and Elimination of replicas, to maximize hypothesis diversity while keeping computational complexity under control.
dc.identifier.citationComputers and Electrical Engineering, 2013; 40(3):884-896
dc.identifier.doi10.1016/j.compeleceng.2013.07.023
dc.identifier.issn0045-7906
dc.identifier.issn1879-0755
dc.identifier.urihttps://hdl.handle.net/1959.8/153841
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.rightsCopyright 2013 Elsevier
dc.source.urihttps://doi.org/10.1016/j.compeleceng.2013.07.023
dc.subjectmultiple hypothesis tracking
dc.titleMixture reduction techniques and probabilistic intensity models for multiple hypothesis tracking of targets in clutter
dc.typeJournal article
pubs.publication-statusPublished
ror.mmsid9915909863101831

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