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dc.contributor.authorGarcia Fernandez, Angel
dc.contributor.authorMorelande, M.
dc.contributor.authorGrajal, J.
dc.date.accessioned2017-06-23T03:00:42Z
dc.date.available2017-06-23T03:00:42Z
dc.date.created2017-06-19T03:39:33Z
dc.date.issued2011
dc.identifier.citationGarcia Fernandez, A. and Morelande, M. and Grajal, J. 2011. Nonlinear filtering update phase via the single point truncated unscented Kalman filter.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/53626
dc.description.abstract

A fast algorithm to approximate the first two moments of the posterior probability density function (pdf) in nonlinear non-Gaussian Bayesian filtering is proposed. If the pdf of the measurement noise has a bounded support and the measurement function is continuous and bijective, we can use a modified prior pdf that meets Bayes' rule exactly. The central idea of this paper is that a Kalman filter applied to a modified prior distribution can improve the estimate given by the conventional Kahnan filter. In practice, bounded support is not required and the modification of the prior is accounted for by adding an extra-point to the set of sigma-points used by the unscented Kalman filter. © 2011 IEEE.

dc.titleNonlinear filtering update phase via the single point truncated unscented Kalman filter
dc.typeConference Paper
dcterms.source.titleFusion 2011 - 14th International Conference on Information Fusion
dcterms.source.seriesFusion 2011 - 14th International Conference on Information Fusion
dcterms.source.isbn9781457702679
curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusFulltext not available


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