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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/4052

Title: Robust filtering with randomly varying sensor delay: The finite-horizon case
Authors: Yang, F
Wang, Z
Feng, G
Liu, X
Keywords: Kalman filtering
Parameter uncertainty
Random sensor delay
Robust filtering
Time-varying systems
Publication Date: 2009
Publisher: IEEE
Citation: IEEE Transactions on Circuits and Systems I. 56 (3): 664-672
Abstract: In this paper, we consider the robust filtering problem for discrete time-varying systems with delayed sensor measurement subject to norm-bounded parameter uncertainties. The delayed sensor measurement is assumed to be a linear function of a stochastic variable that satisfies the Bernoulli random binary distribution law. An upper bound for the actual covariance of the uncertain stochastic parameter system is derived and used for estimation variance constraints. Such an upper bound is then minimized over the filter parameters for all stochastic sensor delays and admissible deterministic uncertainties. It is shown that the desired filter can be obtained in terms of solutions to two discrete Riccati difference equations of a form suitable for recursive computation in online applications. An illustrative example is presented to show the applicability of the proposed method.
Description: Copyright [2009] 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 pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
URI: http://bura.brunel.ac.uk/handle/2438/4052
DOI: http://dx.doi.org/10.1109/TCSI.2008.918009
ISSN: 1549-8328
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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