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dc.contributor.authorGarcia Fernandez, Angel
dc.contributor.authorMorelande, M.
dc.contributor.authorGrajal, J.
dc.identifier.citationGarcia Fernandez, A. and Morelande, M. and Grajal, J. 2014. Bayesian Sequential Track Formation. IEEE Transactions on Signal Processing. 62 (24): pp. 6366-6379.

This paper presents a theoretical framework for track building in multiple-target scenarios from the Bayesian point of view. It is assumed that the number of targets is fixed and known. We propose two optimal methods for building tracks sequentially. The first one uses the labelling of the current multitarget state estimate that minimizes the mean-square labeled optimal subpatternassignment error. This method requires knowledge of the posterior density of the vector-valued state. The second assigns the labeling that maximizes the probability that the current multi-targetstate estimate is optimally linked with the available tracks at the previous time step. In this case, we only require knowledge of the random finite-set posterior density without labels.

dc.subjectTarget labelling
dc.subjectBayesian framework
dc.subjectmultiple target tracking
dc.subjectrandom finite sets
dc.titleBayesian Sequential Track Formation
dc.typeJournal Article
dcterms.source.titleIEEE Transactions on Signal Processing

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curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusOpen access

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