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hal.structure.identifierIRIDA
dc.contributor.authorPACE, Michele
hal.structure.identifierAdvanced Learning Evolutionary Algorithms [ALEA]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorDEL MORAL, Pierre
dc.date.issued2013
dc.identifier.issn1932-4553
dc.description.abstractEnWe discuss a connection between spatial branching processes and the PHD recursion based on conditioning principles for Poisson Point Processes. The branching process formulation gives a generalized Feynman-Kac systems interpretation of the PHD filtering equations, which enables the derivation of mean-field implementations of the PHD filter. This approach provides a principled means for obtaining target tracks and alleviates the need for pruning, merging and clustering for the estimation of multi-target states.
dc.language.isoen
dc.publisherIEEE
dc.title.enMean-Field PHD Filters Based on Generalized Feynman-Kac Flow
dc.typeArticle de revue
dc.identifier.doi10.1109/JSTSP.2013.2250909
dc.subject.halInformatique [cs]/Bibliothèque électronique [cs.DL]
bordeaux.journalIEEE Journal of Selected Topics in Signal Processing
bordeaux.volume7
bordeaux.issue3
bordeaux.peerReviewedoui
hal.identifierhal-00932284
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-00932284v1
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