摘要
A group tracking algorithm for split maneuvering based on complex domain topological descriptions is proposed for the tracking of members in a maneuvering group. According to the split characteristics of a group target, split models of group targets are established based on a sliding window feedback mechanism to determine the occurrence and classification of split maneuvering, which makes the tracked objects focus by group members effectively. The track of an outlier single target is reconstructed by the sequential least square method. At the same time, the relationship between the group members is expressed by the complex domain topological description method, which solves the problem of point-track association between the members. The Singer method is then used to update the tracks. Compared with classical multi-target tracking algorithms based on Multiple Hypothesis Tracking （MHT） and the Different Structure Joint Probabilistic Data Association （DS-JPDA） algorithm, the proposed algorithm has better tracking accuracy and stability, is robust against environmental clutter and has stable time-consumption under both classical radar conditions and partly resolvable conditions.
A group tracking algorithm for split maneuvering based on complex domain topological descriptions is proposed for the tracking of members in a maneuvering group. According to the split characteristics of a group target, split models of group targets are established based on a sliding window feedback mechanism to determine the occurrence and classification of split maneuvering, which makes the tracked objects focus by group members effectively. The track of an outlier single target is reconstructed by the sequential least square method. At the same time, the relationship between the group members is expressed by the complex domain topological description method, which solves the problem of point-track association between the members. The Singer method is then used to update the tracks. Compared with classical multi-target tracking algorithms based on Multiple Hypothesis Tracking （MHT） and the Different Structure Joint Probabilistic Data Association （DS-JPDA） algorithm, the proposed algorithm has better tracking accuracy and stability, is robust against environmental clutter and has stable time-consumption under both classical radar conditions and partly resolvable conditions.
基金
co-supported by the National Natural Science Foundation of China(Nos.61471383,61531020,61471379 and 61102166)
