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dc.contributor.authorDavey, S.-
dc.contributor.authorWieneke, M.-
dc.contributor.authorVu, H.-
dc.identifier.citationIEEE Journal of Selected Topics in Signal Processing, 2013; 7(3):435-447-
dc.description.abstractThe Histogram Probabilistic Multi-Hypothesis Tracker (H-PMHT) is a parametric mixture-fitting approach to track-before-detect. The original implementations of H-PMHT dealt with Gaussian shaped targets with fixed or known extent. More recent applications have addressed other special cases of the target shape. This article reviews these recent extensions and consolidates them into a new unified framework for targets with arbitrary appearance. The framework adopts a stochastic appearance model that describes the sensor response to each target and describes filters and smoothers for several example models. The article also demonstrates that H-PMHT can be interpreted as the decomposition of multi-target track-before-detect into decoupled single target track-before-detect using the notion of associated images. © 2007-2012 IEEE.-
dc.description.statementofresponsibilitySamuel J. Davey, Monika Wieneke, and Han Vu-
dc.publisherInstitute of Electrical and Electronics Engineers-
dc.rights© 2013 British Crown Copyright-
dc.subjectparticle filter-
dc.subjectViterbi algorithm.-
dc.titleHistogram-PMHT unfettered-
dc.typeJournal article-
Appears in Collections:Aurora harvest 4
Electrical and Electronic Engineering publications

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