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Title: Probability-dependent gain-scheduled filtering for stochastic systems with missing measurements
Authors: Wei, G
Wang, Z
Shen, B
Li, M
Keywords: Filtering;Gain scheduling;Missing measurements;Probability-dependent Lyapunov functions;Time-varying Bernoulli distribution
Issue Date: 2011
Publisher: IEEE
Citation: IEEE Transactions on Circuits and Systems II: Express Briefs, 58(11): 753 - 757, Nov 2011
Abstract: This 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.
Description: Copyright @ 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.
ISSN: 1549-7747
Appears in Collections:Electronic and Computer Engineering
Computer Science
Dept of Computer Science Research Papers

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