基于深度聚类的航空交通流识别与异常检测研究  

Study on Air Traffic Flow Recognition and Anomaly Detection Based on Deep Clustering

作　　者：饶丹 时宏伟[1] RAO Dan;SHI Hongwei(School of Computer Science,Sichuan University,Chengdu 610065,China)

机构地区：[1]四川大学计算机学院,成都610065

出　　处：《计算机科学》2023年第3期121-128,共8页Computer Science

摘　　要：针对传统的聚类算法无法捕获高维轨迹数据在低维空间中的隐含关系,且难以定义适当的相似性度量以同时考虑轨迹的局部和全局特征的问题,提出了一种基于深度神经网络的多变量轨迹深度聚类框架(MTDC)并将其用于航空交通流识别与异常检测。该框架主要包含一个非对称的自编码器和一个自定义的轨迹聚类层。自编码器由一维卷积神经网络和双向长短时记忆网络堆叠而成,用于学习原始输入在低维隐空间中的特征表示。轨迹聚类层则通过计算隐空间中样本的Q分布实现聚类。结合自编码器的重建损失和轨迹聚类Q分布定义了一个新的异常分数,用于检测异常轨迹。使用基于广播式自动相关监视(ADS-B)的真实轨迹数据进行实验,结果表明,所提框架能有效地进行航空交通流识别,并能检测出具有实际意义且可解释的异常轨迹。Aiming at the problem that traditional clustering algorithms cannot capture the implicit relationship of high-dimen-sional trajectory data in low-dimensional space, and it is difficult to define appropriate similarity measures to consider both local and global features of trajectories, a multivariate trajectory deep clustering(MTDC) framework based on deep neural network(DNN) is proposed and used for air traffic flow recognition and anomaly detection.The framework mainly includes an asymmetric autoencoder and a custom trajectory clustering layer.The autoencoder is mainly composed of 1D convolutional neural network and bi-directional long short-term memory to learn the feature representation of the original input in the low-dimensional latent space.The trajectory clustering layer realizes clustering by calculating the Q distribution of samples in the hidden space.Combined with reconstruction loss of autoencoder and trajectory clustering Q distribution, a new anomaly score is defined for anomaly trajectory detection.The results of experiments using real trajectory data based on automatic dependent surveillance-broadcast(ADS-B) show that the proposed framework is effective for air traffic flow recognition and can detect anomaly trajectories that are mea-ningful and interpretable.

关 键 词：轨迹聚类 异常检测 深度神经网络 自编码器 ADS-B 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]