Anti-bias Track Association Algorithm Based on Topology Statistical Distance

The topology information of the targets observed by sensors can be used to solve the track association problem under the condition of systematic bias. However, the traditional algorithms dont make full use of track information and are not fit for the presence of sensors false alarm and missing detect. An anti-bias track association algorithm based on topology statistical distance is proposed. First, the target state estimation and covariance is converted to acquire the topology description in the coordinates of the reference target. Then the global optimization association is realized based on the derivation of topology statistical distance. Finally, the average statistic distance of neighboring target association pairs in the coordinates of the reference target is applied as the association degree of the reference targets, and the reference targets association judgment is accomplished according to the double threshold rule. The simulation results show that the performance of the proposed algorithm is apparently better than the traditional algorithm under the conditions of dense formation, random distributed targets and the presence of sensors false alarm and missing detection.