The damped modified iterated Kalman filter for nonlinear discrete time systems

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dc.contributor.authorChoi, UJinko
dc.date.accessioned2013-02-27T12:15:34Z-
dc.date.available2013-02-27T12:15:34Z-
dc.date.created2012-02-06-
dc.date.created2012-02-06-
dc.date.issued1997-02-
dc.identifier.citationKYBERNETIKA, v.33, no.4, pp.387 - 398-
dc.identifier.issn0023-5954-
dc.identifier.urihttp://hdl.handle.net/10203/68510-
dc.description.abstractThe modified iterated Kalman filter, which will be called MIKF for brevity, is derived from the modified Newton method to approximate a maximum likelihood estimate. The MIKF is also obtained by an iteration scheme for the extended kalman filter equations. A convergence analysis of the MIKF is given. By the damping method, we can reduce the total CPU time needed to estimate the state variables or may even obtain a convergent scheme when the MIKF diverges. A numerical example shows the effective convergence behavior of the damped MIKF.-
dc.languageEnglish-
dc.publisherKybernetika-
dc.subjectNEWTON METHOD-
dc.subjectPARAMETER-
dc.titleThe damped modified iterated Kalman filter for nonlinear discrete time systems-
dc.typeArticle-
dc.identifier.wosidA1997YB32200003-
dc.identifier.scopusid2-s2.0-0040061792-
dc.type.rimsART-
dc.citation.volume33-
dc.citation.issue4-
dc.citation.beginningpage387-
dc.citation.endingpage398-
dc.citation.publicationnameKYBERNETIKA-
dc.contributor.localauthorChoi, UJin-
dc.type.journalArticleArticle-
dc.subject.keywordPlusNEWTON METHOD-
dc.subject.keywordPlusPARAMETER-
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