Labeled Multi-Bernoulli Filter based Group Target Tracking Using SDE and Graph Theory

Abstract

Multi-target tracking is an extremely challenging task when targets move in the formation of groups and interact with each other. Group target tracking has to deal with this problem in contrast to independently moving targets as assumed in most multi-target tracking algorithms. A feasible approach for group target tracking is to estimate the group structure and modify the motion model in the prediction step of multi-target tracker according to the group structure. In this paper, we propose an ad hoc labeled multi-Bernoulli (LMB) filter for tracking group target with interaction, which use stochastic differential equation to model the joint motion of group targets and estimate group structure by using graph theory. Simulation results show that the proposed algorithm can estimate the target state more accurately than the traditional method without group motion modification.