南京航空航天大学学报（英文版）2020，Vol.37Issue(4)：574-585,12.
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 Auto?encoder and Its Application in Trajectory Clustering
摘要
Abstract
Anomalous trajectory detection and traffic flow classification for complicated airspace are of vitalimportance 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.关键词
ADS‑B数据/稳健深度自编码器/异常检测/航迹聚类Key words
ADS‑B data/robust deep auto‑encoder/anomaly detection/trajectory clustering分类
信息技术与安全科学引用本文复制引用
董欣放,刘继新,张魏宁,张明华,江灏..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[J].南京航空航天大学学报（英文版）,2020,37(4):574-585,12.基金项目
This work was supported in part by the Foundation of Graduate Innovation Center in NUAA (kfjj20190707). （kfjj20190707）
