终端区飞行轨迹聚类分析及异常轨迹识别.pdf

2022年第22卷第9期
科 学 技 术 与 工 程 IS
of flight trajectories， a clustering method with fast calculation speed， self-identification of original trajectories and elimination of inter­
ference trajectories was proposed. Firstly， the uniform parameterization method was improved to reduce the scale of flight trajectory da­
ta. Secondly, the kernel principal component analysis( KPCA) method was used to reduce the dimensionality of the track and increase
the difference between different types of points to facilitate clustering. Finally， the density-based spatial clustering of applications with
noise( DBSCAN) algorithm was 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 simplifies the pre-processing of flight trajectory before clustering， improves the clustering efficiency， makes the clustering effect
more accurate， and it can identify abnormal trajectory.
[ Keywords] flight trajectory； pattern recognition； cluster analysis； KPCA
近年来， 空中交通流量快速增长， 以往的空域 标准进离场程序设计，帮助管制员优化进离场程
扇区的划分不能满足现有的空中交通流量的需要,