一种适用性广的ADS-B异常数据检测方法  

A Widely Applicable ADS-B Anomaly Data Detection Method

作　　者：陈敏[1] 马志坤 吴仁彪[1] CHEN Min;MA Zhikun;WU Renbiao(Tianjin Key Laboratory for Advanced Signal Processing,Civil Aviation University of China,Tianjin 300300,China)

机构地区：[1]中国民航大学天津市智能信号与图像处理重点实验室,天津300300

出　　处：《信号处理》2023年第5期875-885,共11页Journal of Signal Processing

基　　金：国家自然基金与民航局联合资助重点项目(U2133204);中国民航大学国家自然科学基金配套专项(3122022PT01)。

摘　　要：广播式自动相关监视(ADS-B)系统因其准确性和高效性而广泛应用于空管监视领域,但其依赖于全球导航卫星系统(GNSS)工作,采用明文格式广播数据,且缺乏消息认证和数据加密机制,极易遭受恶意攻击。目前已提出的基于深度学习的ADS-B异常检测模型仅适用于固定航线的航班,或仅对于巡航阶段的航班有效,无法对航班起飞和进近着陆阶段的ADS-B异常数据进行检测,适用范围较窄。论文给出了一种融合K-Means聚类及LSTM神经网络的ADS-B异常数据检测模型,通过对航班进行飞行阶段划分,模型能够有针对性地学习航班各个飞行阶段的航迹特征。对比实验表明,论文提出的混合模型对航班在起飞、巡航及进近着陆各飞行阶段中出现的ADS-B异常数据均具有较高的精确率和召回率,整体检测效果较好,适用性更广,且适用于真实异常事件的检测。Automatic Dependent Surveillance-Broadcast(ADS-B)system is widely used in Air Traffic Surveillance and Management because of its high accuracy and efficiency.However,ADS-B relies on Global Navigation Satellite System(GNSS),and broadcasts messages in plaintext without authentication and encryption.All of these defects make ADS-B system extremely vulnerable to malicious attacks.Several different ADS-B anomaly data detection models based on deep learning were proposed in recent years,but these models are only suitable for the specific flight operations in a fixed route or only effective for the flights in cruise phase,but not for the flights during takeoff and approach phases.In view of this problem,an ADS-B anomaly data detection model which combines K-Means clustering and LSTM neural network is proposed.By dividing the flight trajectory into different flight phases,the model can learn the characteristics of trajectories in different ones.Comparative experiments show that,the hybrid model has a high accuracy and recall rate for ADS-B abnormal data detection in all flight phases of takeoff,cruise and approach landing.The overall detection effect is better,the applicability is wider,and it is suitable for detecting real abnormal events.

关 键 词：广播式自动相关监视 航迹异常检测 长短期记忆人工神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]