Research on Multi-sensor Fusion Algorithm Based on Statistical Track Correlation

First, a multi-sensor sequential track association algorithm is discussed in this paper, and then modified by means of threshold adaptive technique. Due to the correlation of track estimation error among the sensors, the cross-covariance between the local tracks must be considered in the process of track fusion. However, the calculation of cross-covariance is very complicated, and the amount of calculation is very big as well, especially in the condition of existing false association probability when the tracks are being associated. Considering the above factor, a pseudo-measurements combination filtering algorithm based on statistic track association is proposed, having avoided the complexity of cross-covariance computation for distributed system and data association in dense targets and clutter environment for centralized system. It approaches the optimal solution as well. Simulation results show its effectivity.