Receding Horizon Tracking of an Unknown Number of Mobile Targets using a Bearings-Only Sensor

Abstract

Planning the motion of bearings-only sensors is critical for enabling accurate tracking of the positions of moving targets. In this paper, we demonstrate planning the observer's motion over horizons greater than one step for estimating an unknown and varying number of indistinguishable, maneuvering targets of interest using a probability hypothesis density (PHD) filter, with a Rériyi divergence reward for selecting actions. We describe approximations to make this approach computationally feasible, and we propose using Monte Carlo tree search (MCTS) to further reduce the cost. Finally, we present simulation results showing that longer planning horizons reduce the error in the estimates and that MCTS can reduce the cost of planning without sacrificing the quality of the estimates.