Efficient search strategies for non-myopic sensor scheduling in target tracking

We propose two tree pruning algorithms to reduce the computational complexity of non-myopic sensor scheduling for target tracking. We consider a mobile bearings-only sensor that chooses from a finite set of possible moves at each time epoch. The scheduling objective is to select the sequence of sensor moves to minimize a tracking cost over M > 1 future time epochs. Tracking is performed with a particle filter, and expected future costs are calculated using an unscented transform with the particle filter. Simulation shows that the two algorithms significantly reduce the time and memory requirements compared to exhaustive search.