An Anti-Spoofing Model Based on MVM and MCCM for a Loosely-Coupled GNSS/INS /LiDAR Kalman Filter

GNSS/INS/LiDAR-based Multi-Sensor Fusion (MSF) systems facilitate the efficient integration of multiple navigation sensors to deliver stable and dependable positioning outcomes in Autonomous Vehicles (AVs). Generally, AVs are bound to operate in diverse scenarios. However, state-of-the-art spoofing attack algorithms can identify vulnerable periods of MSF systems and execute aggressive GNSS spoofing in certain specific scenarios with high success rates. The present study proposes a security defense algorithm for MSF systems based on an enhanced Kalman filter, incorporating the Measurement Variance Monitoring (MVM) and Minimum Constraint of Covariance Matrix (MCCM) model. MVM effectively constrains the impact of GNSS signals with large positioning errors, thereby mitigating the adverse effects caused by spoofing attacks. MCCM fully leverages LiDAR information between two consecutive GNSS signals to enhance the credibility of LiDAR measurements, thus improving the positioning correction capability. Furthermore, considering the diverse application environments of AVs, this paper primarily focuses on sensor uncertainty variation and validates different models in varying scenarios. Ultimately, real-world data demonstrate the effectiveness of the proposed anti-spoofing model, which can significantly mitigate positioning errors caused by GNSS spoofing attacks without affecting navigation accuracy.