A track association algorithm based on the weighted association graph for laser triangulation sensors

The track association problem is a challenging task in dense target environments due to the existence of furcation tracks, cross tracks and combination tracks. At present, most of the track association algorithms experience a substantial decline in accuracy in processing the above three types of tracks. In response to the defect, a track association algorithm based on the weighted association graph is presented. Information divergence is introduced to construct the weighted association graph. And association quality is acquired based on the weighted idea to reflect the association relationship among tracks. The algorithm solves the association problem with furcation tracks, cross tracks and combination tracks effectively, and improves the performance of association algorithms in dense target environments. The simulation results show the effectiveness and the superiority of the algorithm.