Nonmyopic sensor scheduling for target tracking with emission control

Active sensors obtain the measurements of targets by emitting energy that can be intercepted by enemy surveillance sensors. To satisfy the target tracking requirement and control the whole system emission, we propose a nonmyopic sensor scheduling to minimize the emission cost while maintaining a desired tracking accuracy. The processes of target tracking and emission control are formulated as a partially observable Markov decision process. Then, we translate our scheduling problem to a discrete unconstrained optimization problem, which consists of multistep emission cost and multistep tracking accuracy cost. Furthermore, the cubature Kalman filter is utilized to update the target belief state and predict the multistep tracking accuracy cost, whereas the multistep emission cost is obtained by hidden Markov model filter. Scheduling is implemented efficiently by constructing a decision tree and using a search algorithm, which combines uniform cost search with augmented branch and bound technique. Simulation results demonstrate the effectiveness of our proposed method.