终端区飞行轨迹聚类分析及异常轨迹识别

为有效掌握空中交通流的分布规律,提高飞行轨迹聚类效率与质量,提出了一种精确度高、运算快、自主识别异常轨迹的飞行轨迹聚类方法。首先,改进均匀参数化法来降低了飞行轨迹数据规模。其次,提出一种基于核主成分分析法(kernel principal component analysis,KPCA)飞行轨迹降维方法,突出不同类点之间的差异。最后,采用基于密度空间聚类方法(density-based spatial clustering of applications with noise,DBSCAN)算法剔除飞行干扰轨迹并完成聚类。实验表明,该方法在简化数据预处理的条件下,对1243条飞行轨迹实现准确聚类,划分为识别出6个类别,保持较高的聚类质量并识别异常轨迹。相较于其他聚类方法,本文方法简化了聚类前对飞行轨迹的预处理,提高了聚类效率的同时聚类效果更加准确并能够识别异常轨迹。

Cluster analysis and abnormal trajectory identification of flight trajectory in terminal area

In order to more effectively master the distribution rule of air traffic flow and improve the clustering efficiency and quality of flight trajectories, a clustering method with fast calculation speed, self-identification of original trajectories and elimination of interference trajectories is proposed. First, the resampling method reduces the size of data and arranges the sampling points evenly. Then, the kernel principal component analysis method is used to reduce the dimensionality of the track and increase the difference between different types of points to facilitate clustering. Finally, the DBSCAN algorithm is used to identify and eliminate the interference flight trajectory. Experiments show that this method can eliminate all the disturbing tracks and some of the incomplete tracks that affect clustering. Compared with other clustering methods, the method in this paper simplifies the pre-processing of the flight trajectory before clustering, improves the clustering efficiency and at the same time the clustering effect is more accurate and identify abnormal trajectories.