Abstract
Target tracking is a fundamental problem in robotics research and has been the subject of detailed studies over the years. In this paper, we introduce a new formulation, based on the mathematical concept of random finite sets, that allows for tracking an unknown and dynamic number of mobile targets with a team of robots. We show how to employ the Probability Hypothesis Density filter to simultaneously estimate the number of targets and their positions. Next, we present a greedy algorithm for assigning trajectories to the robots to actively track the targets. We prove that the greedy algorithm is a 2-approximation for maximizing submodular tracking objective functions. We examine two such functions: the mutual information between the estimated target positions and future measurements from the robots, and the expected number of targets detected by the robot team. We provide extensive simulation evaluations using a real-world dataset.
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