航迹点特征的时间窗分割算法的航迹聚类

现有航迹聚类算法未考虑到航空器航向变化和高度下降等因素对聚类结果的影响,同时聚类过程中缺乏时间信息,另外实测二次雷达数据中存在离群点异常数据,离群点的存在会影响最终的聚类效果,使得聚类结果不准确。提出基于航迹点特征的时间窗分割算法,将航空器进场的航向变化值以及高度下降值作为确定聚类簇大小的影响因素,对进场航空器航迹点数量进行时间窗分割。对真实的进场二次雷达数据仿真分析,从仿真结果中可以看出当影响因子a为0.4时,航迹的曲率最小,聚类效果最好,进而采用层次聚类算法对不同LOF值所对应的航迹点进行聚类,得到最后的聚类结果可以为管制员现场指挥提供技术指导。

Research on Track Clustering of Time Window Segmentation Algorithm Based on Track Point Characteristics

Aimed at the problem that the existing track clustering algorithm does not take into account the influence of aircraft heading change and altitude drop on the clustering results, at the same time, the clustering process lacks time information, and in addition, outlier data exist in the two radar data, and the existence of outliers will affect the final clustering result, leading to inaccurate clustering results, this paper proposes a time window segmentation algorithm based on feature point track. The algorithm is to take the aircraft heading change approach value and height decreased value as the influence factors of determining cluster size, the paper segments the number of aircraft entering the track point time window segmentation. The simulation analysis of real approach two radar data shows that when the influence factor is 0.4, the curvature of track is minimum, and the clustering effect is the best, then the hierarchical clustering algorithm is used to cluster the tracks corresponding to different values, and the final clustering results are obtained, providing technical guidance for the controller''s scene command.