随机有限集理论及其在多目标跟踪中的应用和实现

梳理了随机有限集(RFS)的理论基础和发展脉络,重点对其在多目标跟踪中应用和实现的难点问题进行详细分析.首先针对单传感器情形,深入讨论RFS的几类典型近似技术,包括:概率假设密度(PHD)滤波器、势概率假设密度(CPHD)滤波器、多伯努利(MeMBer)滤波器以及泛化标签多伯努利(GLMB)滤波器,对其发展脉络进行分析,并对高斯混合(GM)及序贯蒙特卡罗(SMC)实现中面临的问题进行研究;其次,针对多传感器情形,介绍时空配准问题的处理方法,并分别从集中式、分布式融合两个方面对基于RFS多传感器多目标跟踪技术进行分析;再次,对RFS滤波器在实际应用中面临的困难及挑战进行分析;最后,基于现有研究进展,提出RFS在多目标跟踪领域未来需重点关注及研究的方向.

Random finite set: Theory, application and implementation for multi-target tracking

This paper reviews the theoretical basis and the state-of-art development of the random finite set, emphasis on the difficulties in application and implementation for multi-target tracking. Firstly, for the single sensor case, several typical approximation techniques based on the random finite set(RFS) are discussed, including probability hypothesis density(PHD), cardinalized PHD(CPHD), multi-target multi-Bernoulli(MeMBer), and generalized labeled multi- Bernoulli(GLMB). The development context of the filters is analyzed, and the problems in implementation with Gaussian mixture(GM) and sequential Monte Carlo(SMC) are studied. Then for the multi-sensor case, the processing method of the multi-sensor spatial registration is introduced, and the application of the RFS filter is studied from two aspects: centralized and distributed fusion. In addition, the difficulties and challenges of the RFS filter in practice are analyzed. Finally, based on the recent researches, some future research directions which need to be focused on for the RFS in multi-target tracking are introduced.