A novel track-to-track association algorithm in distributed sensor network

Focused on improvement on computational speed of track-to-track association in distributed sensor network, a novel multi-source fuzzy track-to-track association algorithm based on temporary tracks and source relative credibility is proposed in this paper. Firstly, in the global fusion center, temporary tracks are formed by two measurements of the same local track from the same local fusion node, then system tracks are generated by the fusion of temporary tracks with system tracks, and track-to-track association is between temporary tracks and system tracks. In addition, source relative credibility is introduced to use as the association rule that only the temporary track from the maximum relative credible source can be selected to associate with the system track when there exist several temporary tracks associated with a system track. By applying the proposed method to the simulation experiment of track-to-track association, the simulation results show that it can determine targets' tracks accurately, has better performance in the computational speed of track-to-track association and the precision of system tracks than the traditional fuzzy association method, and is an effective and feasible multi-sensor track association method.