Distributed state estimation for hidden Markov models by sensor networks with dynamic quantization

Author

Huang, Minyi ; Dey, Subhrakanti

Author_Institution

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

fYear

2004

fDate

14-17 Dec. 2004

Firstpage

355

Lastpage

360

Abstract

This paper considers the state estimation of hidden Markov models by sensor networks. We study a network structure with feedback from the fusion center to the sensor nodes, and a dynamic quantization scheme is proposed and analyzed by a stochastic control approach. The resulting dynamic programming equation is solved by the relative value iteration algorithm. Furthermore, a dynamic rate allocation method is also proposed.

Keywords

dynamic programming; feedback; hidden Markov models; iterative methods; sensor fusion; state estimation; stochastic programming; wireless sensor networks; distributed state estimation; dynamic programming equation; dynamic quantization; dynamic rate allocation method; feedback; fusion center; hidden Markov models; relative value iteration algorithm; sensor networks; stochastic control; Capacitive sensors; Dynamic programming; Equations; Feedback; Hidden Markov models; Quantization; Random processes; Sensor fusion; Sensor phenomena and characterization; State estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Sensors, Sensor Networks and Information Processing Conference, 2004. Proceedings of the 2004

Print_ISBN

0-7803-8894-1

Type

conf

DOI

10.1109/ISSNIP.2004.1417488

Filename

1417488