基于深度自编码器的终端区异常航迹识别方法及其在航迹聚类中的应用

Identifying Anomaly Aircraft Trajectories in Terminal Areas Based on Deep Autoencoder and Its Application in Trajectory Clustering

异常航迹识别与交通流分类对复杂空域的安全与效率分析是重要的。一些研究人员使用基于密度的无监督聚类算法提取空域中这两种与管制行为相关的航迹数据。然而,数据质量问题和交通流之间的微小密度差异是这项工作的两个主要难点。为了解决这两个问题,本文提出一种结合稳健自编码器模型(Robust deep auto-encoder,RDAE)和密度峰值(Density peak,DP)聚类算法的框架。具体地,通过不同的正则化优化方式使得RDAE模型分别用来重构去噪航迹与异常航迹检测。然后,RDAE模型的Encoder输出的非线性降维向量作为DP聚类算法的输入以分类空域中全局的交通流。在含有标签的广州白云机场数据集上的实验表明,所提算法能够自动地捕捉到空域内飞机运动的非常规时空交通模式。RDAE在异常航迹检测以及所提框架在交通流分类上的优越性均通过可视化与定量的结果评估分析。

Anomalous trajectory detection and traffic flow classification for complicated airspace are of vital importance to safety and efficiency analysis.Some researchers employed density-based unsupervised machine learning method to exploit these trajectories related to air traffic control(ATC)actions.However,the quality of position data and the tiny density difference between traffic flows in the terminal area make it particularly challenging.To alleviate these two challenges,this paper proposes a novel framework which combines robust deep auto-encoder(RDAE)model and density peak(DP)clustering algorithm.Specifically,the RDAE model is utilized to reconstruct denoising trajectory and identify anomaly trajectories in the terminal area by two different regularizations.Then,the nonlinear components captured by the encoder of RDAE are input in the DP algorithm to classify the global traffic flows.An experiment on a terminal airspace at Guangzhou Baiyun Airport(ZGGG)with anomaly label shows that the proposed combination can automatically capture non-conventional spatiotemporal traffic patterns in the aircraft movement.The superiority of RDAE and combination are also demonstrated by visualizing and quantitatively evaluating the experimental results.