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dc.contributor.authorWang, Z-
dc.contributor.authorQiao, H-
dc.identifier.citationSignal Processing, IEEE Transactions on [see also Acoustics, Speech, and Signal Processing, IEEE Transactions on]. 50 (3) 560 - 567en
dc.descriptionCopyright [2002] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to By choosing to view this document, you agree to all provisions of the copyright laws protecting it.-
dc.description.abstractThis paper deals with the robust filtering problem for uncertain bilinear stochastic discrete-time systems with estimation error variance constraints. The uncertainties are allowed to be norm-bounded and enter into both the state and measurement matrices. We focus on the design of linear filters, such that for all admissible parameter uncertainties, the error state of the bilinear stochastic system is mean square bounded, and the steady-state variance of the estimation error of each state is not more than the individual prespecified value. It is shown that the design of the robust filters can be carried out by solving some algebraic quadratic matrix inequalities. In particular, we establish both the existence conditions and the explicit expression of desired robust filters. A numerical example is included to show the applicability of the present method.en
dc.format.extent146986 bytes-
dc.subjectBilinear stochastic systems; discrete-time systemsen
dc.subjectquadratic matrix inequalities; robust filteringen
dc.subjectuncertain systemsen
dc.titleRobust filtering for bilinear uncertain stochastic discrete-time systemsen
dc.typeResearch Paperen
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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