Multi-sensor distributed control strategy for multi-target tracking

Distributed sensor network technology plays an extremely important role in complex multi-target tracking system. Aiming at distributed sensor control problem in multi-sensor multi-target tracking, this paper proposes some multi-sensor control strategies information-based. First, a multi-sensor multi-Bernoulli filter is presented by using random finite set (RFS), and a multi-sensor multi-Bernoulli density is approximated by a set of parameterized multi-Bernoulli process. Further, through the sequential Monte Carlo implementation of the multi-Bernoulli filter, the sampling scheme is designed to sample the multi-Bernoulli density, and then the multi-target state space distribution is approximated by a set of weighted particles. Subsequently, the Bhattacharyya distance, as the reward function, is used for the decision making of independent and parallel multi-sensor control. As another important part, this paper proposes a multi-sensor control strategy based on multi-target tactical significance assessment, where the goal is to evaluate multi-target tactical significance and then track preferentially the maximum threat target. Finally, the simulations verify the effectiveness of the proposed algorithms. © 2019, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.