Dynamic Sensor Management for Multisensor Multitarget Tracking
Yi Li, Lucas W. Krakow, Edwin K. P. Chong, Kenneth N. Groom · 2006
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.