Show simple item record

dc.contributor.authorGarcia-Fernandez, Angel
dc.contributor.authorSvensson, Lennart
dc.contributor.authorMorelande, M.
dc.contributor.authorSarkka, S.
dc.date.accessioned2017-01-30T10:47:25Z
dc.date.available2017-01-30T10:47:25Z
dc.date.created2016-02-24T19:30:20Z
dc.date.issued2015
dc.identifier.citationGarcia-Fernandez, A. and Svensson, L. and Morelande, M. and Sarkka, S. 2015. Posterior Linearization Filter: Principles and Implementation Using Sigma Points. IEEE Transactions on Signal Processing. 63 (20): pp. 5561-5573.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/5634
dc.identifier.doi10.1109/TSP.2015.2454485
dc.description.abstract

This paper is concerned with Gaussian approximations to the posterior probability density function (PDF) in the update step of Bayesian filtering with nonlinear measurements. In this setting, sigma-point approximations to the Kalman filter (KF) recursion are widely used due to their ease of implementation and relatively good performance. In the update step, these sigma-point KFs are equivalent to linearizing the nonlinear measurement function by statistical linear regression (SLR) with respect to the prior PDF. In this paper, we argue that the measurement function should be linearized using SLR with respect to the posterior rather than the prior to take into account the information provided by the measurement. The resulting filter is referred to as the posterior linearization filter (PLF). In practice, the exact PLF update is intractable but can be approximated by the iterated PLF (IPLF), which carries out iterated SLRs with respect to the best available approximation to the posterior. The IPLF can be seen as an approximate recursive Kullback-Leibler divergence minimization procedure. We demonstrate the high performance of the IPLF in relation to other Gaussian filters in two numerical examples.

dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.titlePosterior Linearization Filter: Principles and Implementation Using Sigma Points
dc.typeJournal Article
dcterms.source.volume63
dcterms.source.number20
dcterms.source.startPage5561
dcterms.source.endPage5573
dcterms.source.issn1053-587X
dcterms.source.titleIEEE TRANSACTIONS ON SIGNAL PROCESSING
curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusFulltext not available


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record