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dc.contributor.authorGarcia Fernandez, Angel
dc.contributor.authorMorelande, M.
dc.contributor.authorGrajal, J.
dc.date.accessioned2018-02-06T06:16:27Z
dc.date.available2018-02-06T06:16:27Z
dc.date.created2018-02-06T05:49:52Z
dc.date.issued2012
dc.identifier.citationGarcia Fernandez, A. and Morelande, M. and Grajal, J. 2012. Mixture truncated unscented Kalman filtering, pp. 479-486.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/63304
dc.description.abstract

This paper proposes a computationally efficient nonlinear filter that approximates the posterior probability density function (PDF) as a Gaussian mixture. The novelty of this filter lies in the update step. If the likelihood has a bounded support made up of different regions, we can use a modified prior PDF, which is a mixture, that meets Bayes' rule exactly. The central idea of this paper is that a Kalman filter applied to each component of the modified prior mixture can improve the approximation to the posterior provided by the Kalman filter. In practice, bounded support is not necessary. © 2012 ISIF (Intl Society of Information Fusi).

dc.titleMixture truncated unscented Kalman filtering
dc.typeConference Paper
dcterms.source.startPage479
dcterms.source.endPage486
dcterms.source.title15th International Conference on Information Fusion, FUSION 2012
dcterms.source.series15th International Conference on Information Fusion, FUSION 2012
dcterms.source.isbn9780982443859
curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusFulltext not available


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