Identification and Tracking of Multi-group Targets in Circular Formation under Multi-sensor Networks
Feng Yang, Jingru Niu, Lihong Shi, Litao Zheng · 2024
To address the challenges posed by structure identification, data transmission and information fusion in distributed group target tracking, this paper proposes a novel distributed structure identification and tracking algorithm for resolvable group targets with circular formations. The proposed algorithm combines the Joint Probabilistic Data Association algorithm and the K-medoids clustering method within each sensor to estimate all target states and partition them into different subgroups. Then, the circular formation of each subgroup is identified based on its geometric features. In addition, the Consensus on Information is introduced to fuse each local information after matching the states of group targets across multi-sensor networks. Simulation results demonstrate the effectiveness of the proposed algorithm.