一种基于深度混合密度网络的航空器轨迹异常检测方法

• 作 者: 陈丽晶 曾维理 羊钊
• 机 构: 南京航空航天大学民航学院,南京 211106,中国;故障诊断与健康管理航空科技重点实验室,上海 201601,中国;南京航空航天大学民航学院,南京 211106,中国
• 出 处: 南京航空航天大学学报（英文版）
• 发表日期: 2021年5期
• 关键词: aircraft trajectory anomaly detection mixture density network long short-term me

及时、准确的异常航空器轨迹检测是提高飞行安全的关键.然而,目前用于异常检测的统计和机器学习方法不能很好地表征航空器轨迹的特征.飞行轨迹数据的高维性、异质性和时序性,导致异常检测精度较低.因此,本文提出了一种基于深度混合密度网络(Deep mixture density network,DMDN)的异常轨迹检测方法,可以对飞行轨迹安全性进行评估,并检测具有异常数据模式的飞行.该技术由两部分组成:利用深度长短时记忆(Long short?term memory,LSTM)网络对飞行轨迹特征进行有效编码;利用高斯混合模型(Gaussian mixture model,GMM)参数化飞行轨迹的统计特性.在广州白云国际机场终端区空域的实验结果表明,该方法能够有效地捕获航空器飞行轨迹的统计特性.该模型能够检测风险较高的异常航班,其性能优于两种主流方法,可作为空中交通管制的辅助决策工具.“,”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.