Quickest Change Detection in Hidden Markov Models for Sensor Networks

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 talk, 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 researchis also given. This is a joint work with Dr. Yajun Mei