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dc.contributor.authorWei, G-
dc.contributor.authorWang, Z-
dc.contributor.authorShen, B-
dc.contributor.authorLi, M-
dc.identifier.citationIEEE Transactions on Circuits and Systems II: Express Briefs, 58(11): 753 - 757, Nov 2011en_US
dc.descriptionCopyright @ 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en_US
dc.description.abstractThis brief addresses the gain-scheduled filtering problem for a class of discrete-time systems with missing measurements, nonlinear disturbances, and external stochastic noise. The missing-measurement phenomenon is assumed to occur in a random way, and the missing probability is time-varying with securable upper and lower bounds that can be measured in real time. The multiplicative noise is a state-dependent scalar Gaussian white-noise sequence with known variance. The addressed gain-scheduled filtering problem is concerned with the design of a filter such that, for the admissible random missing measurements, nonlinear parameters, and external noise disturbances, the error dynamics is exponentially mean-square stable. The desired filter is equipped with time-varying gains based primarily on the time-varying missing probability and is therefore less conservative than the traditional filter with fixed gains. It is shown that the filter parameters can be derived in terms of the measurable probability via the semidefinite program method.en_US
dc.description.sponsorshipThis work was supported in part by the Leverhulme Trust of the U.K., the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the National Natural Science Foundation of China under Grants 61028008, 61074016 and 60974030, the Shanghai Natural Science Foundation of China under Grant 10ZR1421200, and the Alexander von Humboldt Foundation of Germany.en_US
dc.subjectGain schedulingen_US
dc.subjectMissing measurementsen_US
dc.subjectProbability-dependent Lyapunov functionsen_US
dc.subjectTime-varying Bernoulli distributionen_US
dc.titleProbability-dependent gain-scheduled filtering for stochastic systems with missing measurementsen_US
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pubs.organisational-data/Brunel/Brunel (Active)/School of Info. Systems, Comp & Maths-
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pubs.organisational-data/Brunel/Brunel Active Staff/School of Information Systems, Computing and Mathematics-
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Appears in Collections:Electronic and Computer Engineering
Computer Science
Dept of Computer Science Research Papers

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