Simulation of CBMeMber Multi-target Tracking Algorithm Based on Gauss Mixture

Abstract

Multi-target tracking technologies have important research value in many fields. Algorithms based on random finite set theory can achieve a better tracking effect without data association, which have attracted wide attentions. In this paper, after establishing a real multi-target motion scenario, CBMeMBer filtering algorithm is simulated and implemented on the linear Gauss condition, and is compared with PHD, CPHD and MeMBer filtering algorithm. The simulation results show that CBMeMBer filtering algorithm is correct and effective. Under the same simulation conditions, its tracking performance is obviously improved, and it has good application prospects in multi-target tracking field.