Title :
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.
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;
Journal_Title :
Signal Processing, IEEE Transactions on
DOI :
10.1109/TSP.2006.874809
