基于载波相位和IMU的地面车辆GNSS欺骗检测

Zachary Clements, James E. Yoder, T. Humphreys
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引用次数: 8

摘要

本文开发、实现并验证了一种强大的单天线载波相位测试,用于检测配备低成本惯性测量单元(IMU)的地面车辆上的全球导航卫星系统(GNSS)欺骗攻击。日益自动化的地面车辆需要精确定位,以适应不寻常的自然或意外事件,并防止蓄意攻击。本文的欺骗检测技术利用了由校准良好的载波相位差分GNSS (CDGNSS)估计器产生的载波相位固定模糊剩余成本,该估计器与低成本IMU紧密耦合。载波相位固定模糊剩余成本在亚厘米级对载波相位测量值与先前测量值和基于IMU测量值和车辆约束的动力学模型预测值之间的差异非常敏感。这种差异在欺骗攻击中会出现,因为攻击者实际上无法预测由道路不规则性引起的厘米振幅车辆运动。所开发的欺骗检测方法的有效性通过在德克萨斯州奥斯汀的车载传感器套件捕获的数据进行了评估。该数据集包括消费级和工业级IMU数据,以及一组不同的多路径环境(开放天空、浅层城市和深层城市)。注入数据集的人工最坏情况欺骗攻击在两秒内被检测到。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Carrier-phase and IMU based GNSS Spoofing Detection for Ground Vehicles
This paper develops, implements, and validates a powerful single-antenna carrier-phase-based test to detect Global Navigation Satellite Systems (GNSS) spoofing attacks on ground vehicles equipped with a low-cost inertial measurement unit (IMU). Increasingly-automated ground vehicles require precise positioning that is resilient to unusual natural or accidental events and secure against deliberate attack. This paper’s spoofing detection technique capitalizes on the carrier-phase fixed-ambiguity residual cost produced by a well-calibrated carrier-phase-differential GNSS (CDGNSS) estimator that is tightly coupled with a low-cost IMU. The carrier-phase fixed-ambiguity residual cost is sensitive at the sub-centimeter-level to discrepancies between measured carrier phase values and the values predicted by prior measurements and by the dynamics model, which is based on IMU measurements and on vehicle constraints. Such discrepancies will arise in a spoofing attack due to the attacker’s practical inability to predict the centimeter-amplitude vehicle movement caused by roadway irregularities. The effectiveness of the developed spoofing detection method is evaluated with data captured by a vehicle-mounted sensor suite in Austin, Texas. The dataset includes both consumerand industrial-grade IMU data and a diverse set of multipath environments (open sky, shallow urban, and deep urban). Artificial worst-case spoofing attacks injected into the dataset are detected within two seconds.
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