Non-myopic sensor scheduling to track multiple reactive targets

This study addresses the sensor scheduling problem of selecting and assigning sensors dynamically for multi-target tracking. The authors goal is to trade off the tracking accuracy and the interception risk in a period of time. The interception risk is incurred by the fact that the emission energy originating from a sensor can be intercepted by the target during the tracking mission. To react to sensor emission, the targets are able to switch between dynamic models. This non-myopic sensor scheduling problem is formulated as a partially observable Markov decision process, where the one-step reward is constructed by combining the tracking error with the interception probability and the information state is tracked by the interacting multiple model extended Kalman filtering. A novel sampling approach using the unscented transformation is proposed for long-term reward approximation. Numerical simulations illustrate the validity of the proposed scheduling scheme.