Stochastic Detectability and Mean Bounded Error Covariance of the Recursive Kalman Filter with Markov Jump Parameters

Costa, Eduardo F.; Astolfi, Alessandro

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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Stochastic Detectability and Mean Bounded Error Covariance of the Recursive Kalman Filter with Markov Jump Parameters

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Author(s):	Costa, Eduardo F. ^[1] ; Astolfi, Alessandro ^{[2, 3]} Total Authors: 2
Affiliation:	^[1] USP, ICMC, Dept Matemat Aplicada & Estat, BR-13560970 Sao Carlos, SP - Brazil ^[2] Univ Roma Tor Vergata, Dipartimento Informat Sistemi & Prod, Rome - Italy ^[3] Univ London Imperial Coll Sci Technol & Med, Dept Elect & Elect Engn, London - England Total Affiliations: 3
Document type:	Journal article
Source:	Stochastic Analysis and Applications; v. 28, n. 2, p. 190-201, 2010.
Web of Science Citations:	2
Abstract
In this article, we study the error covariance of the recursive Kalman filter when the parameters of the filter are driven by a Markov chain taking values in a countably infinite set. We do not assume ergodicity nor require the existence of limiting probabilities for the Markov chain. The error covariance matrix of the filter depends on the Markov state realizations, and hence forms a stochastic process. We show in a rather direct and comprehensive manner that this error covariance process is mean bounded under the standard stochastic detectability concept. Illustrative examples are included. (AU)

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