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dc.contributor.authorMahler, Ronald
dc.date.accessioned2017-08-24T02:20:21Z
dc.date.available2017-08-24T02:20:21Z
dc.date.created2017-08-23T07:21:50Z
dc.date.issued2015
dc.identifier.citationMahler, R. 2015. Tracking 'bunching' multitarget correlations, pp. 102-109.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/55757
dc.identifier.doi10.1109/MFI.2015.7295793
dc.description.abstract

© 2015 IEEE. In point process theory, permanental processes are used to model statistical populations whose members tend to be attracted to each other ('bunch'). This paper initiates what appears to be the first application of permanental processes to multitarget detection and tracking. Permanental processes can be used to construct bivariate-Poisson models of statistical correlations between two Poisson multitarget populations. We introduce a recursive Bayes filter for such permanentally-correlated multitarget systems. Then, by analogy with the probability hypothesis density (PHD) filter, we derive first-order approximate filter equations. This permanental-PHD filter requires the (removable) assumption that probability of detection is unity.

dc.titleTracking 'bunching' multitarget correlations
dc.typeConference Paper
dcterms.source.volume2015-October
dcterms.source.startPage102
dcterms.source.endPage109
dcterms.source.titleIEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
dcterms.source.seriesIEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
dcterms.source.isbn9781479977727
curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusFulltext not available


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