Scheduling multiple sensors for tracking a highly maneuvering target in clutter

The problem of multiple sensor scheduling for tracking a highly maneuvering target in clutter is considered. The objective is to schedule the sensors one or multiple time steps ahead so that the overall tracking performance of the system can be improved while minimizing the cost of resources. In the proposed scheduling algorithm, under the constraint that only one sensor may be used at any time step, we predict the expected cost one or multiple time steps ahead as a function of the candidate sensor scheduling sequences, and pick the sequence that minimizes an expected performance metric. We use a random sampling approach coupled with switching multiple kinematic models for target motion, to generate future (pseudo-)states and (pseudo-) measurements which allows computation of the relevant performance metric. Tracking of highly maneuvering target is achieved by an effective suboptimal filtering algorithm based on an interacting multiple model (IMM) filtering approach combined with probabilistic data association (PDA) technique and the proposed sensor scheduling scheme. The proposed algorithm is illustrated via a simulation example involving two geographically distributed radar sensors.