Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/118941
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dc.contributor.authorSun, Q.-
dc.contributor.authorLim, C.-
dc.contributor.authorShi, P.-
dc.contributor.authorLiu, F.-
dc.date.issued2019-
dc.identifier.citationIEEE Transactions on Automatic Control, 2019; 64(3):1109-1124-
dc.identifier.issn0018-9286-
dc.identifier.issn1558-2523-
dc.identifier.urihttp://hdl.handle.net/2440/118941-
dc.description.abstractThis paper presents a moving horizon algorithm with mode detection for state estimation in Markov jump systems with Gaussian noise. This state estimation scheme is a combination of the maximum-likelihood algorithm and the moving horizon approach. The maximum-likelihood algorithm provides optimal estimate of the mode sequence within a moving fixed-size horizon, and the moving horizon estimation is an optimization-based solution. As a result, a mode detection-moving horizon estimator design method is proposed. Through the stochastic observability properties of the Markov jump linear systems, sufficient conditions for stability are established.-
dc.description.statementofresponsibilityQing Sun, Cheng-Chew Lim, Peng Shi and Fei Liu-
dc.language.isoen-
dc.publisherInstitute of Electrical and Electronics Engineers-
dc.rights© 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications standards/publications/rights/index.html for more information.-
dc.source.urihttp://dx.doi.org/10.1109/tac.2018.2816102-
dc.subjectMarkov jump systems; maximum-likelihood algorithm; moving horizon approach; state estimation-
dc.titleDesign and stability of moving horizon estimator for Markov jump linear systems-
dc.typeJournal article-
dc.identifier.doi10.1109/TAC.2018.2816102-
dc.relation.granthttp://purl.org/au-research/grants/arc/DP170102644-
pubs.publication-statusPublished-
dc.identifier.orcidLim, C. [0000-0002-2463-9760]-
dc.identifier.orcidShi, P. [0000-0001-8218-586X]-
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Electrical and Electronic Engineering publications

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