摘要
基于卫星导航的列车自主定位是列车控制系统等铁路关键装备的重要技术方向。然而,列车卫星定位面临诸多挑战,除信号可视性问题和多径效应之外,来自系统外部的蓄意欺骗等干扰攻击,会对定位功能及性能产生直接威胁。为此,本文以基于全球导航卫星系统(GNSS)的列车定位面临的伪距欺骗这一典型干扰模式为对象,研究并提出一种基于样本增强的伪距欺骗主动检测方法。该方法运用Wasserstein生成式对抗网络(WGAN)解决受欺骗干扰样本数据不均衡问题,利用扩充的数据集训练检测模型,并引入自注意力(SA)机制优化来自不同接收机输入特征之间的相对位置关系,采用生成式对抗学习思想形成一套完整的列车卫星定位伪距欺骗干扰检测方案。由列车卫星定位欺骗干扰注入测试结果可知,提出的方法能够充分运用生成式对抗网络思想解决受欺骗样本的典型受限问题,融合自注意力机制所得检测性能显著优于载噪比检测和代表性机器学习算法等常规检测方案;对建模样本未覆盖特征具备良好的适应能力,具有更优的检测精度和鲁棒性,在多个伪距欺骗干扰模式数据集上测试所得F1分数均超过0.99。该方法在欺骗干扰检测性能方面的优势能够为众多卫星导航系统铁路应用提供有力支撑,为有效防范卫星定位在信息安全层面的攻击入侵提供了有利条件。
Satellite-based autonomous train positioning is an important technical direction for train control systems and other key equipment in railway applications.However,satellite-based train positioning has to face many challenges.In addition to signal visibility and multipath effect,intentional spoofing and other interference attacks from the outside would directly threaten the positioning function and the achieved performance level.This paper takes the pseudo-range spoofing,which is a typical interference mode for train positioning based on the Global Navigation Satellite System(GNSS),as the study object and proposes an active detection method based on sample augmentation.This method adopted the Wasserstein Generative Adversarial Networks(WGAN)to solve the problem of imbalanced spoofing affected samples.It trained a detection model using the expanded datasets and introduces a Self-Attention(SA)mechanism to optimize the relative positional relationship between the input features from different GNSS receivers.A complete detection scheme for the pseudo-range-mode GNSS spoofing to satellite-based train positioning was established based on generative adversarial learning.According to the results from the pseudo-range GNSS spoofing injection tests to the satellite-based train positioning,the proposed method can make full use of the Generative Adversarial Network solution to solve the typical problem caused by limited spoofing samples.The detection performance derived with the self-attention mechanism is significantly enhanced over the typical conventional detection methods.It realizes the adaptability to the features not covered by the modeling samples,with enhanced detection accuracy and robustness.The F1 score obtained in the test with multiple pseudo-range GNSS spoofing mode datasets exceeded 0.99.The advantages of the proposed method in spoofing detection performance can provide great support for many GNSS-based railway applications and enable favorable conditions for effectively protecting attacks to GNSS at the information security level.
作者
刘江
张楚
蔡伯根
王剑
陆德彪
LIU Jiang;ZHANG Chu;CAI Baigen;WANG Jian;LU Debiao(School of Automation and Intelligence,Beijing Jiaotong University,Beijing 100044,China;Beijing Engineering Research Center of EMC and GNSS Technology for Rail Transportation,Beijing Jiaotong University,Beijing 100044,China;Frontiers Science Center for Smart High-speed Railway System,Beijing Jiaotong University,Beijing 100044,China)
出处
《交通运输系统工程与信息》
EI
CSCD
北大核心
2024年第3期32-42,共11页
Journal of Transportation Systems Engineering and Information Technology
基金
国家自然科学基金委员会—中国国家铁路集团有限公司铁路基础研究联合基金(U2268206)
国家自然科学基金(T2222015)
北京市自然科学基金(4232031)。
关键词
智能交通
伪距欺骗检测
样本增强
列车定位
全球导航卫星系统
生成式对抗网络
intelligent transportation
pseudo-range spoofing detection
sample augmentation
train positioning
global navigation satellite system
generative adversarial network
