This paper proposes a novel type of random finite set (RFS), namely augmented RFS, to address the problem of resolvable group target tracking, which integrates the information of both the group attributes and the dynamic state of group targets into random finite sets. Specifically, we initially introduce an augmented random finite set framework, incorporating group labels and group cardinality to estimate both the trajectories and states of group targets. Then, a new multi-target filter based on the augmented RFS is proposed to achieve the process of group target tracking. Finally, simulation experiments are conducted to demonstrate the effectiveness of the proposed filter in group target tracking scenarios.

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.