Show simple item record

dc.contributor.authorGarcia-Fernandez, Angel
dc.contributor.authorVo, Ba-Ngu
dc.date.accessioned2017-01-30T12:44:40Z
dc.date.available2017-01-30T12:44:40Z
dc.date.created2016-02-23T19:30:20Z
dc.date.issued2015
dc.identifier.citationGarcia-Fernandez, A. and Vo, B. 2015. Derivation of the PHD and CPHD Filters Based on Direct Kullback-Leibler Divergence Minimization. IEEE Transactions on Signal Processing. 63 (21): pp. 5812-5820.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/24733
dc.identifier.doi10.1109/TSP.2015.2468677
dc.description.abstract

In this paper, we provide novel derivations of the probability hypothesis density (PHD) and cardinalised PHD (CPHD) filters without using probability generating functionals or functional derivatives. We show that both the PHD and CPHD filters fit in the context of assumed density filtering and implicitly perform Kullback-Leibler divergence (KLD) minimizations after the prediction and update steps. We perform the KLD minimizations directly on the multitarget prediction and posterior densities.

dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.titleDerivation of the PHD and CPHD Filters Based on Direct Kullback-Leibler Divergence Minimization
dc.typeJournal Article
dcterms.source.volume63
dcterms.source.number21
dcterms.source.startPage5812
dcterms.source.endPage5820
dcterms.source.issn1053-587X
dcterms.source.titleIEEE TRANSACTIONS ON SIGNAL PROCESSING
curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusFulltext not available


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record