Abstract:

A non-myopic scheduling problem of how to intelligently and dynamically select active/passive sensors in a single platform for target tracking is studied. The goal is to develop a nonmyopic scheme to make an optimal tradeoff between the tracking accuracy and the radiation risk in a period of time. The problem is formulated as a partially observable Markov decision process (POMDP) to solve target tracking and emission control together. The cubature sampling is proposed to evaluate the longterm accuracy reward, and the longterm radiation cost is derived from the hidden Markov model filter. The original problem is transformed into a decision tree and solved using the branch and bound method. Simulation results demonstrate the effectiveness of the proposed scheduling approach.