Tracking Multiple Resolvable Group Targets With Coordinated Motion via Labeled Random Finite Sets
Qinchen Wu, Jinping Sun, Bin Yang, Tao Shan, Yanping Wang · IEEE Transactions on Signal Processing · 2025
The standard multi-target transition density assumes that, conditional on the current multi-target state, targets survive and move independently of each other. Although this assumption is followed by most multi-target tracking (MTT) algorithms, it may not be applicable for tracking group targets exhibiting coordinated motion. This paper presents a principled Bayesian solution to tracking multiple resolvable group targets in the labeled random finite set framework. The transition densities of group targets with collective behavior are derived both for single-group and multi-group. For single-group, the transition density is characterized by a general labeled multi-target density and then approximated by the closest general labeled multi-Bernoulli (GLMB) density in terms of Kullback-Leibler divergence. For multi-group, we augment the group structure to multi-target states and propose a multiple group structure transition model (MGSTM) to recursively infer it. Additionally, the conjugation of the structure augmented multi-group multi-target density is also proved. An efficient implementation of multi-group multi-target tracker, named MGSTM-LMB filter, and its Gaussian mixture form are devised which preserves the first-order moment of multi-group multi-target density in recursive propagation. Numerical simulation results demonstrate the capability of the proposed MGSTM-LMB filter in multi-group scenes.