Multiple Hypothesis Group Target Tracking Using Hypergraph Matching

A multiple hypothesis group target tracking algorithm using hypergraph matching is proposed in this paper to achieve efficient data association in group target tracking. Firstly, the observations are clustered into different groups and each group is tracked as a whole to get the group estimation. By introducing the delay decision, the group splitting and merging behaviors are detected based on the group track hypothesis trees generating in the delay period. Then, based on the fact that the targets belong to a group are usually keep stable relative position, hypergraph matching algorithm is applied to make use of these relative spatial information to aid the data association of closely spaced targets in a group. The simulation results prove that the multiple hypothesis group target tracking algorithm proposed in this paper can obtain better estimation of the groups’ states. Besides, the hypergraph matching algorithm also achieves better performance of data association among the targets in a group, because of the extra spatial information.