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.