Quickest Detection of a Moving Target in a Sensor Network

To be considered for the 2019 IEEE Jack Keil Wolf ISIT Student Paper Award. The problem of quickest detection of a moving target in sensor networks is studied. At some unknown time, a target emerges in the sensor network, and one of the sensors in the network is affected, whose data generating distribution undergoes a change. It is assumed that as the target moves around in the sensor network, the sensor that is affected by the target changes with time. Specifically, if a sensor becomes unaffected, then its data generating distribution changes back to the pre-change mode. A discrete time Markov chain is used to model the location of the affected sensor, and thus the data generating distribution of the sensor network after the target emerges is a hidden Markov model. The goal is to detect the existence of the target as quickly as possible subject to false alarm constraints. A windowed test based on a generalized likelihood ratio approach is constructed, and its asymptotic optimality is further established. Numerical results are provided to demonstrate its performance.