南京航空航天大学学报（英文版）2021，Vol.38Issue(5)：840-851,12.
一种基于深度混合密度网络的航空器轨迹异常检测方法
An Aircraft Trajectory Anomaly Detection Method Based on Deep Mixture Density Network
摘要
Abstract
The timely and accurately detection of abnormal aircraft trajectory is critical to improving flight safety. However,the existing anomaly detection methods based on machine learning cannot well characterize the features of aircraft trajectories. Low anomaly detection accuracy still exists due to the high-dimensionality,heterogeneity and temporality of flight trajectory data. To this end,this paper proposes an abnormal trajectory detection method based on the deep mixture density network (DMDN) to detect flights with unusual data patterns and evaluate flight trajectory safety. The technique consists of two components:Utilization of the deep long short-term memory (LSTM)network to encode features of flight trajectories effectively,and parameterization of the statistical properties of flight trajectory using the Gaussian mixture model(GMM). Experiment results on Guangzhou Baiyun International Airport terminal airspace show that the proposed method can effectively capture the statistical patterns of aircraft trajectories. The model can detect abnormal flights with elevated risks and its performance is superior to two mainstream methods. The proposed model can be used as an assistant decision-making tool for air traffic controllers.关键词
航空器轨迹/异常检测/混合密度网络/长短期记忆/高斯混合模型Key words
aircraft trajectory/anomaly detection/mixture density network/long short-term memory(LSTM)/Gaussian mixture model(GMM)分类
航空航天引用本文复制引用
陈丽晶,曾维理,羊钊..一种基于深度混合密度网络的航空器轨迹异常检测方法[J].南京航空航天大学学报（英文版）,2021,38(5):840-851,12.基金项目
This work was supported in part by the National Natural Science Foundation of China(Nos.62076126,52075031),and the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.SJCX19_0013). （Nos.62076126,52075031）
