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dc.contributor.authorSun, Q.-
dc.contributor.authorLim, C.-
dc.contributor.authorLiu, F.-
dc.identifier.citationInternational Journal of Systems Science, 2017; 48(5):1009-1021-
dc.description.abstractWe discuss the state estimation advantages for a class of linear discrete-time stochastic jump systems, in which a Markov process governs the operation mode, and the state variables and disturbances are subject to inequality constraints. The horizon estimation approach addressed the constrained state estimation problem, and the Bayesian network technique solved the stochastic jump problem. The moving horizon state estimator designed in this paper can produce the constrained state estimates with a lower error covariance than under the unconstrained counterpart. This new estimation method is used in the design of the restricted state estimator for two practical applications.-
dc.description.statementofresponsibilityQing Sun, Cheng-Chew Lim, Fei Liu-
dc.publisherTaylor & Francis-
dc.rights© 2016 Informa UK Limited, trading as Taylor & Francis Group-
dc.subjectFilter; stochastic jump systems; state estimation; moving horizon estimation; constraints-
dc.titleConstrained state estimation for stochastic jump systems: moving horizon approach-
dc.typeJournal article-
dc.identifier.orcidLim, C. [0000-0002-2463-9760]-
Appears in Collections:Aurora harvest 3
Electrical and Electronic Engineering publications

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