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dc.contributor.authorGarcia Fernandez, Angel
dc.contributor.authorMorelande, M.
dc.contributor.authorGrajal, J.
dc.date.accessioned2017-06-23T03:01:51Z
dc.date.available2017-06-23T03:01:51Z
dc.date.created2017-06-19T03:39:33Z
dc.date.issued2011
dc.identifier.citationGarcia Fernandez, A. and Morelande, M. and Grajal, J. 2011. Particle filter for extracting target label information when targets move in close proximity.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/53912
dc.description.abstract

This paper addresses the problem of approximating the posterior probability density function of two targets after a crossing from the Bayesian perspective such that the information about target labels is not lost. To this end, we develop a particle filter that is able to maintain the inherent multimodality of the posterior after the targets have moved in close proximity. Having this approximation available, we are able to extract information about target labels even when the measurements do not provide information about target's identities. In addition, due to the structure of our particle filer, we are able to use an estimator that provides lower optimal subpattern assignment (OSPA) errors than usual estimators. © 2011 IEEE.

dc.titleParticle filter for extracting target label information when targets move in close proximity
dc.typeConference Paper
dcterms.source.titleFusion 2011 - 14th International Conference on Information Fusion
dcterms.source.seriesFusion 2011 - 14th International Conference on Information Fusion
dcterms.source.isbn9781457702679
curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusFulltext not available


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