DocumentCode
1167296
Title
On Filtering of Markov Chains in Strong Noise
Author
Chigansky, Pavel
Author_Institution
Dept. of Math., Weizmann Inst. of Sci., Rehovot
Volume
52
Issue
9
fYear
2006
Firstpage
4267
Lastpage
4272
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
Markov processes; filtering theory; least mean squares methods; maximum likelihood estimation; signal denoising; MAP filtering estimate; MMSE; asymptotic expression; conditional measure concentration; filtering theory; finite-state Markov chain; invariant signal distribution; minimum a posteriori probability; minimum mean square error; Atomic measurements; Density measurement; Equations; Filtering; Hidden Markov models; Loss measurement; Mean square error methods; Probability; Random variables; Recursive estimation; Error asymptotic; hidden Markov models; nonlinear filtering;
fLanguage
English
Journal_Title
Information Theory, IEEE Transactions on
Publisher
ieee
ISSN
0018-9448
Type
jour
DOI
10.1109/TIT.2006.880042
Filename
1683947
Link To Document