Robust Minimum Variance Filtering

Uri Shaked, Carlos E. de Souza

Research output: Contribution to journalArticlepeer-review

186 Scopus citations

Abstract

This paper deals with the robust minimum variance filtering problem for linear systems subject to norm-bounded parameter uncertainty in both the state and the output matrices of the state-space model. The problem addressed is the design of linear filters having an error variance with a guaranteed upper bound for any allowed uncertainty. Two methods for designing robust filters are investigated. The first one deals with constant parameter uncertainty and focuses on the design of steady-state filters that yield an upper bound to the worst-case asymptotic error variance. This bound depends on an upper bound for the power spectrum density of a signal at a specific point in the system, and it can be made tighter if a tight bound on the latter power spectrum can be obtained. The second method allows for time-varying parameter uncertainty and for general time-varying systems and is more systematic. We develop filters with an optimized upper bound for the error variance for both finite and infinite horizon filtering problems.

Original languageEnglish
Pages (from-to)2474-2483
Number of pages10
JournalIEEE Transactions on Signal Processing
Volume43
Issue number11
DOIs
StatePublished - Nov 1995

Funding

FundersFunder number
Tel-Aviv University
Australian Research Council

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