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

1 Scopus citations

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

Access to Document

10.1109/TIT.2006.880042

Cite this

@article{04b7c4e1e3f545e1975a8e5b29e057a9,

title = "On filtering of Markov chains in strong noise",

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.",

keywords = "Error asymptotic, Hidden Markov models, Nonlinear filtering",

author = "Pavel Chigansky",

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",

year = "2006",

month = sep,

doi = "10.1109/TIT.2006.880042",

language = "אנגלית",

volume = "52",

pages = "4267--4272",

journal = "IEEE Transactions on Information Theory",

issn = "0018-9448",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "9",

}

TY - JOUR

T1 - On filtering of Markov chains in strong noise

AU - Chigansky, Pavel

N1 - 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

PY - 2006/9

Y1 - 2006/9

N2 - 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.

AB - 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.

KW - Error asymptotic

KW - Hidden Markov models

KW - Nonlinear filtering

UR - https://www.scopus.com/pages/publications/33748541281

U2 - 10.1109/TIT.2006.880042

DO - 10.1109/TIT.2006.880042

M3 - ???researchoutput.researchoutputtypes.contributiontojournal.article???

AN - SCOPUS:33748541281

SN - 0018-9448

VL - 52

SP - 4267

EP - 4272

JO - IEEE Transactions on Information Theory

JF - IEEE Transactions on Information Theory

IS - 9

ER -

On filtering of Markov chains in strong noise

Abstract

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this