Multisensor Multitarget Tracking and Collision Avoidance Based on the Labeled RFS Void Probability and KL-PaC Cost Function

Existing sensor control methods for multisensor and multitarget tracking seldom consider comprehensive control strategy and collision avoidance between sensors and targets or between sensors together. In this article, we first propose a cost function for the multisensor control strategy, which includes Kullback–Leibler (K–L) divergence, detection probability, and sensor usage cost (KL-PaC cost function). Second, to solve collision avoidance between sensors and targets, we propose a collision avoidance strategy based on the labeled random finite set (RFS) void probability. The strategy is essentially to set up a safe region around the sensor or target and then combine the void probability and cost function in the safe region as the evaluation function. The final simulation and experimental results verify the effectiveness of the proposed approach.