Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/39823
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dc.contributor.authorGray, D.-
dc.contributor.authorDavey, S.-
dc.contributor.authorStreit, R.-
dc.date.issued2002-
dc.identifier.citationProceedings of the 2001 Workshop on Defence Applications of Signal Processing, July 2002, Barossa Valley, Australia / pp. 74-78.-
dc.identifier.urihttp://hdl.handle.net/2440/39823-
dc.description.abstractWhen tracking more than one object a key problem is that of associating measurements with particular tracks. Recently, powerful statistical approaches such as Probabilistic Multi-Hypothesis Tracking (PMHT) and Probabilistic Least Squares Tracking have been proposed to solve the problem of measurement to track association. However, in practice other information may often be available, typically classification measurements from automatic target recognition algorithms, which help associate certain measurements with particular tracks. An extension to the Bayesian PMHT approach which allows noisy classification measurements to be incorporated in the tracking and association process is presented. Example results are given to illustrate the performance improvement that can result from this approach.-
dc.description.statementofresponsibilityD A Gray , S J Davey , and R L Streit-
dc.description.urihttp://www.dasp.ws/2002/Abstracts/Gra.htm-
dc.language.isoen-
dc.titleIncorporating classifications in the PMHT-
dc.typeConference paper-
dc.contributor.conferenceWorkshop on Defence Applications of Signal Processing (2002 : Barossa Valley, South Australia)-
pubs.publication-statusPublished-
Appears in Collections:Aurora harvest 6
Electrical and Electronic Engineering publications

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