To master the complex and changeable spatial distribution characteristics of air traffic flow in a terminal area accurately, and to evaluate and optimize arrival and departure procedures effectively, the cluster problem of 3D real flight trajectories in a terminal area was addressed using a resampling technique.A clustering method that has high computation speed, good expandability, and strong dependability was also proposed.First, based on resampling and principal component analysis method, projection of high-dimensional trajectory data to low dimension was implemented based on the premise of maintaining flight characteristics.Then, flight trajectory cluster analysis and outlier trajectory detection models were presented based on the MeanShift method.Finally, the proposed method was verified using real flight trajectory data of terminal areas in order to analyse the effect of every parameter on cluster results.Experimental results show that principal components having a 96.16％ accumulative contribution rate can be obtained in 0.004 s.Flight trajectory data can be well approximated by the principal components.Compared with the hierarchical clustering method, the proposed method can obtain more dependable flight trajectory clustering results which correspond to the standard aiTival routes.Low similarity trajectories were detected as outliers.%为了准确掌握终端区空中交通流复杂多变的空间分布特征,有效评估、优化进离场程序,基于重采样技术研究了终端区三维真实飞行轨迹的聚类问题,提出了一种计算速度快、可扩展性强、可信度高的聚类方法.首先,结合重采样和主成分分析方法,将高维轨迹数据在保留飞行特征的前提下映射到低维空间;其次,基于MeanShift方法建立飞行轨迹聚类分析与异常轨迹提取模型;最后,利用终端区的真实飞行轨迹数据进行实例验证,并分析模型中各个参数对聚类结果的影响.研究结果表明:该方法耗时0.004 s得到累计贡献率为96.16％的主成分,较好地逼近原始飞行轨迹数据;相较于层次聚类法,本文方法得到的飞行轨迹聚类结果具有更高的可信度,能够准确对应机场标准进场航线设置,并将相似度较低的飞行轨迹提取为异常轨迹.
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