Dynamic Sensor Management for Multisensor Multitarget Tracking

We study the problem of sensor scheduling for multisensor multitarget tracking-to determine which sensors to activate over time to trade off tracking error with sensor usage costs. Formulating this problem as a partially observable Markov decision process (POMDP) gives rise to a non-myopic sensor-scheduling scheme. Our method combines sequential multisensor joint probabilistic data association (MS-JPDA) and particle filtering for belief-state estimation, and uses simulation-based Q-value approximation method for "lookahead". The example of focus in this paper involves the activation of multiple sensors simultaneously for tracking multiple targets, illustrating the effectiveness of our approach.