MARS让无人机在惯性传感器受攻击时自动检测并恢复飞行
MARS: Defending Unmanned Aerial Vehicles From Attacks on Inertial Sensors with Model-based Anomaly Detection and Recovery
- 基于扩展卡尔曼滤波构建抗攻击状态估计器
- 实测可使无人机在攻击后仍完成任务
- 适合需要高可靠性的无人机防御系统研发者
无人飞行器（UAV）依赖惯性测量单元（IMU）维持稳定飞行，但其易受声共振和电磁干扰等物理攻击，导致立即坠毁。为此，我们提出基于模型的异常检测与恢复系统（MARS），实现对惯性传感器攻击的快速检测与动态飞行恢复。MARS采用改进的扩展卡尔曼滤波器，融合位置、速度、航向和电机转速信息，重构精确的姿态与角速度数据用于控制。同时，统计异常检测系统实时监控IMU数据，一旦发现攻击即触发系统级警报。收到警报后，多阶段动态恢复策略暂停任务，将无人机稳定至悬停状态，随后在抗攻击控制下继续执行任务。在PX4软硬件在环环境及真实搭载MARS-PX4飞控的无人机上测试均表明，该方法显著优于现有IMU防护框架，具备攻击生存与任务完成能力。
原文摘要 · Abstract (English)
Unmanned Aerial Vehicles (UAVs) rely on measurements from Inertial Measurement Units (IMUs) to maintain stable flight. However, IMUs are susceptible to physical attacks, including acoustic resonant and electromagnetic interference attacks, resulting in immediate UAV crashes. Consequently, we introduce a Model-based Anomaly detection and Recovery System (MARS) that enables UAVs to quickly detect adversarial attacks on inertial sensors and achieve dynamic flight recovery. MARS features an attack-resilient state estimator based on the Extended Kalman Filter, which incorporates position, velocity, heading, and rotor speed measurements to reconstruct accurate attitude and angular velocity information for UAV control. Moreover, a statistical anomaly detection system monitors IMU sensor data, raising a system-level alert if an attack is detected. Upon receiving the alert, a multi-stage dynamic flight recovery strategy suspends the ongoing mission, stabilizes the drone in a hovering condition, and then resumes tasks under the resilient control. Experimental results in PX4 software-in-the-loop environments as well as real-world MARS-PX4 autopilot-equipped drones demonstrate the superiority of our approach over existing IMU-defense frameworks, showcasing the ability of the UAVs to survive attacks and complete the missions.
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