基于拓扑统计距离的航迹抗差关联算法

Anti-bias Track Association Algorithm Based on Topology Statistical Distance

传感器观测目标的拓扑信息可用于解决系统误差下的航迹关联问题,但传统方法对航迹信息利用不足且难以适应传感器虚警和漏报的情形。论文提出一种基于拓扑统计距离的航迹抗差关联算法,首先转换目标状态估计及其协方差以得到目标参照系下的拓扑描述;然后在推导拓扑统计距离的基础上,进行全局最优关联;最后以目标参照系下邻居目标关联对的平均统计距离作为参照目标间的关联度,根据双门限准则完成参照目标的关联判决。仿真结果表明,在密集编队目标、随机分布目标和传感器存在虚警漏报条件下,该算法的性能明显优于传统方法。

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 don’t make full use of track information and are not fit for the presence of sensor’s 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 target’s 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 sensor’s false alarm and missing detection.