CBMeMBer Filter based Resolvable Group Target Tracking via Graph Theory and Leader-Follower Model

Resolvable group target tracking is of great challenge due to the complex motion interaction between group targets, which leads to tracking performance degradation. To solve this problem, a cardinality-balanced multi-target multi-Bernoulli filter based on the graph theory and leader-follower model is proposed. In the proposed filter, firstly, the group targets are divided into leaders and followers by mean of the leader-follower model. Furthermore, the graph theory is used to establish the state transition equations between those divided group targets. Lastly, the process of state prediction is given, and its corresponding implementation is derived by Gaussian mixture approximations. Simulation experiments verify the superiority and effectiveness of the proposed filter.