基于随机有限集理论的多目标跟踪方法, 能够避免数据关联步骤的困扰, 能够较好地解决复杂环境中目标数目未知且随时间变化的多目标跟踪问题。本文分析基于数据关联和基于随机集理论的多目标跟踪方法, 阐明基于随机集理论的多目标跟踪方法的特点和优点, 对目标状态提取、航迹关联、更准确的滤波算法, 以及复杂条件下的 PHDF算法等关键问题进行总结和评述, 并指出该领域今后的研究热点。

Multi-target tracking methods based on random finite set theory can avoid problems appeared in the progress of data association. It can also deal with multi-target tracking problems in the complex environment with target number unknown and target number varying with time. In this work, multi-target tracking methods based on data association and random finite set are analyzed. Characters and advantages of tracking methods based on random finite set are described. Some key problems, like target state extraction, track-to-track association, more accurate filtering algorithms, and PHDF algorithms under complex situations, are summarized and reviewed, and then some research focuses in the following years are given.