On filtering of Markov chains in strong noise

Pavel Chigansky

doi:10.1109/TIT.2006.880042

On filtering of Markov chains in strong noise

Pavel Chigansky^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

The filtering problem for finite-state Markov chains is revisited in the low signal-to-noise regime. We give a description of conditional measure concentration around the invariant distribution of the signal and derive asymptotic expressions for the performance indices of the minimum mean square error (MMSE) and minimum a posteriori probability (MAP) filtering estimates.

Original language	English
Pages (from-to)	4267-4272
Number of pages	6
Journal	IEEE Transactions on Information Theory
Volume	52
Issue number	9
DOIs	https://doi.org/10.1109/TIT.2006.880042
State	Published - Sep 2006
Externally published	Yes

Bibliographical note

Funding Information:
Manuscript received September 23, 2005; revised May 18, 2006. The research was supported by a grant from the Israel Science Foundation. The author is with the Department of Mathematics, The Weizmann Institute of Science, Rehovot 76100, Israel (e-mail: [email protected]). Communicated by X. Wang, Associate Editor for Detection and Estimation. Digital Object Identifier 10.1109/TIT.2006.880042

Keywords

Error asymptotic
Hidden Markov models
Nonlinear filtering

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10.1109/TIT.2006.880042

Cite this

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On filtering of Markov chains in strong noise

Abstract

Bibliographical note

Keywords

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