摘要
Track association of multi-target has been recognized as one of the key technologies in distributed multiple-sensor data fusion system,and its accuracy directly impacts on the performance of the whole tracking system.A multi-sensor data association is proposed based on aftinity propagation(AP)algorithm.The proposed method needs an initial similarity,a distance between any two points,as a parameter,therefore,the similarity matrix is calculated by track position,velocity and azimuth of track data.The approach can automatically obtain the optimal classification of uncertain target based on clustering validity index.Furthermore,the same kind of data are fused based on the variance of measured data and the fusion result can be taken as a new measured data of the target.Finally,the measured data are classified to a certain target based on the nearest neighbor ideas and its characteristics,then filtering and target tracking are conducted.The experimental results show that the proposed method can effectively achieve multi-sensor and multi-target track association.
Track association of multi-target has been recognized as one of the key technologies in distributed multiple-sensor data fusion system, and its accuracy directly impacts on the performance of the whole tracking system. A multi-sensor data association is proposed based on aftinity propagation （AP） algorithm. The proposed method needs an initial similarity, a distance between any two points, as a parameter, therefore, the similarity matrix is calculated by track position, velocity and azimuth of track data. The approach can automatically obtain the optimal classification of uncertain target based on clustering validity index. Furthermore, the same kind of data are fused based on the variance of measured data and the fusion result can be taken as a new measured data of the target. Finally, the measured data are classified to a certain target based on the nearest neighbor ideas and its characteristics, then filtering and target tracking are conducted. The experimental results show that the proposed method can ef- fectively achieve multi-sensor and multi-target track association.
基金
Supported by the National Natural Science Foundation of China(11078001)
