Quickest Change Detection in Hidden Markov Models for Sensor Networks
نویسندگان
چکیده
The decentralized quickest change detection problem is studied in sensor networks, where a set of sensors receive observations from a hidden Markov model (HMM) 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 primary goal of this paper is to investigate how to choose the best stationary quantizers in the context of quickest change detection in sensor networks. A closely related goal of this paper is to report the distribution of the run length to false alarm for HMM in some scenarios.
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