Sensor Management for Multitarget Collaborative Tracking Using CPCRLB, MCPA, and HBPSO
Wanli Wu, Tao Yan, Guoqiang Sun, Jiajie Shen, Yueqi Yang, Yidan Xu · IEEE Internet of Things Journal · 2025
In multi-target collaborative tracking, sensor resources allocation is a crucial yet complicated problem. A new sensor management algorithm using the Conditional Posterior Cramér-Rao Lower Bound (CPCRLB) as information metric and the Modified Closest Point of Approach (MCPA) algorithm to assess the threat of targets is proposed in this paper. To quantify the tracking performance, CPCRLB is computed based on Particle Filter and serves as the information metric for sensor management. The MCPA threat, which integrates the relative positions and velocities of the target with respect to the protected asset, is employed to guide the sequence of multi-target sensor allocation scheme in order to achieve high tracking accuracy in consideration of target threat. In order to meet the computation requirements, the Hierarchical Binary Particle Swarm Optimization (HBPSO) algorithm is used to search for the optimal sensor allocation scheme at each time step. By integrating a hierarchical architecture into the original Binary Particle Swarm Optimization algorithm, particles from different layers are used to search in different spaces to prevent the algorithm from getting stuck in locally optimal solutions. Simulations in both static and dynamic scenarios demonstrate that this new sensor management algorithm outperforms the existing algorithms, including the state-of-the-art algorithms based on Modified Particle Swarm Optimization (MPSO).