Optimal stationary binary quantizer for decentralized quickest change detection in hidden Markov models

Cheng Der Fuh; Yajun Mei

doi:10.1109/ICIF.2008.4632301

Optimal stationary binary quantizer for decentralized quickest change detection in hidden Markov models

Cheng Der Fuh, Yajun Mei

Global Public Health

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The decentralized quickest change detection problem is studied in sensor networks, where a set of sensors receive observations from a hidden Markov model X and send sensor messages to a central processor, called the fusion center, which makes a final decision when observations are stopped. It is assumed that the parameter θ in the hidden Markov model for X changes from θ₀ to θ₁ at some unknown time. The problem is to determine the policies at the sensor and fusion center levels to jointly optimize the detection delay subject to the average run length (ARL) to false alarm constraint. In this article, a CUSUM-type fusion rule with stationary binary sensor messages is studied and a simple method for choosing the optimal local sensor thresholds is introduced. Further research is also given.

Original language	English (US)
Title of host publication	Proceedings of the 11th International Conference on Information Fusion, FUSION 2008
DOIs	https://doi.org/10.1109/ICIF.2008.4632301
State	Published - 2008
Event	11th International Conference on Information Fusion, FUSION 2008 - Cologne, Germany Duration: Jun 30 2008 → Jul 3 2008

Publication series

Name	Proceedings of the 11th International Conference on Information Fusion, FUSION 2008

Conference

Conference	11th International Conference on Information Fusion, FUSION 2008
Country/Territory	Germany
City	Cologne
Period	6/30/08 → 7/3/08

Keywords

Asymptotic optimality
CUSUM
Hidden Markov models
Multi-sensor
Quantization
Sensor networks
Sequential detection

ASJC Scopus subject areas

Computational Theory and Mathematics
Computer Science Applications
Information Systems

Access to Document

10.1109/ICIF.2008.4632301

Cite this

Optimal stationary binary quantizer for decentralized quickest change detection in hidden Markov models. / Fuh, Cheng Der; Mei, Yajun.
Proceedings of the 11th International Conference on Information Fusion, FUSION 2008. 2008. 4632301 (Proceedings of the 11th International Conference on Information Fusion, FUSION 2008).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Fuh, CD & Mei, Y 2008, Optimal stationary binary quantizer for decentralized quickest change detection in hidden Markov models. in Proceedings of the 11th International Conference on Information Fusion, FUSION 2008., 4632301, Proceedings of the 11th International Conference on Information Fusion, FUSION 2008, 11th International Conference on Information Fusion, FUSION 2008, Cologne, Germany, 6/30/08. https://doi.org/10.1109/ICIF.2008.4632301

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abstract = "The decentralized quickest change detection problem is studied in sensor networks, where a set of sensors receive observations from a hidden Markov model X and send sensor messages to a central processor, called the fusion center, which makes a final decision when observations are stopped. It is assumed that the parameter θ in the hidden Markov model for X changes from θ0 to θ1 at some unknown time. The problem is to determine the policies at the sensor and fusion center levels to jointly optimize the detection delay subject to the average run length (ARL) to false alarm constraint. In this article, a CUSUM-type fusion rule with stationary binary sensor messages is studied and a simple method for choosing the optimal local sensor thresholds is introduced. Further research is also given.",

keywords = "Asymptotic optimality, CUSUM, Hidden Markov models, Multi-sensor, Quantization, Sensor networks, Sequential detection",

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AB - The decentralized quickest change detection problem is studied in sensor networks, where a set of sensors receive observations from a hidden Markov model X and send sensor messages to a central processor, called the fusion center, which makes a final decision when observations are stopped. It is assumed that the parameter θ in the hidden Markov model for X changes from θ0 to θ1 at some unknown time. The problem is to determine the policies at the sensor and fusion center levels to jointly optimize the detection delay subject to the average run length (ARL) to false alarm constraint. In this article, a CUSUM-type fusion rule with stationary binary sensor messages is studied and a simple method for choosing the optimal local sensor thresholds is introduced. Further research is also given.

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Optimal stationary binary quantizer for decentralized quickest change detection in hidden Markov models

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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