Probability of Error Analysis for Hidden Markov Model Filtering With Random Packet Loss

Author

Leong, Alex S C ; Dey, Subhrakanti ; Evans, Jamie S.

Author_Institution

Dept. of Electr. & Electron. Eng., Melbourne Univ., Parkville, Vic.

Volume

55

Issue

3

fYear

2007

fDate

3/1/2007 12:00:00 AM

Firstpage

809

Lastpage

821

Abstract

This paper studies the probability of error for maximum a posteriori (MAP) estimation of hidden Markov models, where measurements can be either lost or received according to another Markov process. Analytical expressions for the error probabilities are derived for the noiseless and noisy cases. Some relationships between the error probability and the parameters of the loss process are demonstrated via both analysis and numerical results. In the high signal-to-noise ratio (SNR) regime, approximate expressions which can be more easily computed than the exact analytical form for the noisy case are presented

Keywords

error analysis; filtering theory; hidden Markov models; maximum likelihood estimation; MAP estimation; SNR; error analysis probability; hidden Markov model filtering; maximum a posteriori estimation; random packet loss; signal-to-noise ratio; Error analysis; Error probability; Estimation error; Filtering; Hidden Markov models; Loss measurement; Signal processing algorithms; Signal to noise ratio; State estimation; State-space methods; Hidden Markov model; observation losses; probability of error; state estimation;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/TSP.2006.888056

Filename

4099560