Dynamic Quantizer Design for Hidden Markov State Estimation Via Multiple Sensors With Fusion Center Feedback

Author

Huang, Minyi ; Dey, Subhrakanti

Author_Institution

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

Volume

54

Issue

8

fYear

2006

Firstpage

2887

Lastpage

2896

Abstract

This paper considers the state estimation of hidden Markov models by sensor networks. The objective is to minimize the long term average of the mean square estimation error for the underlying finite state Markov chain. By employing feedback from the fusion center, a dynamic quantization scheme for the sensor nodes is proposed and analyzed by a stochastic control approach. Dynamic rate allocation is also considered when the sensor nodes generate mode dependent measurements

Keywords

distributed sensors; hidden Markov models; mean square error methods; quantisation (signal); sensor fusion; state estimation; stochastic systems; dynamic quantization scheme; dynamic quantizer design; dynamic rate allocation; finite state Markov chain; fusion center feedback; hidden Markov model state estimation; mean square estimation error; mode dependent measurements; multiple sensors; sensor networks; stochastic control approach; Computer networks; Distributed computing; Dynamic programming; Hidden Markov models; Quantization; Random processes; Sensor fusion; Sensor systems and applications; State estimation; State feedback; Dynamic programming equation; dynamic quantization; hidden Markov models; sensor networks; state estimation;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/TSP.2006.874809

Filename

1658245