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

Huang , Minyi; Dey, Subhrakanti


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.

CollectionsANU Research Publications
Date published: 2006
Type: Journal article
Source: IEEE Transactions on Signal Processing
DOI: 10.1109/TSP.2006.874809


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