Dynamic quantizer design for hidden Markov state estimation via multiple sensors with fusion center feedback

Minyi Huang; Subhrakanti Dey

doi:10.1109/TSP.2006.874809

Dynamic quantizer design for hidden Markov state estimation via multiple sensors with fusion center feedback

Minyi Huang^*, Subhrakanti Dey

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

38 Citations (Scopus)

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.

Original language	English
Pages (from-to)	2887-2896
Number of pages	10
Journal	IEEE Transactions on Signal Processing
Volume	54
Issue number	8
DOIs	https://doi.org/10.1109/TSP.2006.874809
Publication status	Published - Aug 2006

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title = "Dynamic quantizer design for hidden Markov state estimation via multiple sensors with fusion center feedback",

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 = "Dynamic programming equation, Dynamic quantization, Hidden Markov models, Sensor networks, State estimation",

author = "Minyi Huang and Subhrakanti Dey",

year = "2006",

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AU - Dey, Subhrakanti

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AB - 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.

KW - Dynamic programming equation

KW - Dynamic quantization

KW - Hidden Markov models

KW - Sensor networks

KW - State estimation

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DO - 10.1109/TSP.2006.874809

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JO - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

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ER -

Dynamic quantizer design for hidden Markov state estimation via multiple sensors with fusion center feedback

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