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dc.contributor.authorGarcía-Fernández, Angel
dc.contributor.authorSvensson, Lennart
dc.date.accessioned2017-01-30T10:56:20Z
dc.date.available2017-01-30T10:56:20Z
dc.date.created2016-02-24T19:30:20Z
dc.date.issued2015
dc.identifier.citationGarcía-Fernández, A. and Svensson, L. 2015. Gaussian MAP Filtering Using Kalman Optimization. IEEE Transactions on Automatic Control. 60 (5): pp. 1336-1349.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/6909
dc.identifier.doi10.1109/TAC.2014.2372909
dc.description.abstract

© 1963-2012 IEEE. This paper deals with the update step of Gaussian MAP filtering. In this framework, we seek a Gaussian approximation to the posterior probability density function (PDF) whose mean is given by the maximum a posteriori (MAP) estimator. We propose two novel optimization algorithms which are quite suitable for finding the MAP estimate although they can also be used to solve general optimization problems. These are based on the design of a sequence of PDFs that become increasingly concentrated around the MAP estimate. The resulting algorithms are referred to as Kalman optimization (KO) methods. We also provide the important relations between these KO methods and their conventional optimization algorithms (COAs) counterparts, i.e., Newton's and Levenberg-Marquardt algorithms. Our simulations indicate that KO methods are more robust than their COA equivalents.

dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.titleGaussian MAP Filtering Using Kalman Optimization
dc.typeJournal Article
dcterms.source.volume60
dcterms.source.number5
dcterms.source.startPage1336
dcterms.source.endPage1349
dcterms.source.issn0018-9286
dcterms.source.titleIEEE Transactions on Automatic Control
curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusFulltext not available


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