Clock Drift Monitoring Based GNSS Spoofing Detection Method for Autonomous Vehicles

Global Navigation Satellite System (GNSS) is crucial for autonomous systems, providing Position, Velocity, and Timing (PVT) information for platforms such as autonomous vehicles, unmanned aerial vehicles (UAVs), and ships. However, GNSS is vulnerable to spoofing attacks that can manipulate PVT information, bringing severe security risks and disrupting the decisionmaking processes of these systems. Leveraging Doppler measurements is a common method for detecting GNSS spoofing in autonomous vehicles, as spoofing introduces a Doppler bias caused by the relative motion between the user and the spoofer. This anomalous bias serves as a strong indicator of spoofing attacks. Previous Doppler-based detection methods mainly focus on detecting the Doppler bias directly from measurements. However, the bias remains embedded within the raw measurements, necessitating additional efforts for its extraction, which limits the practical applicability of these methods. To overcome these challenges, this paper proposes an indirect method for spoofing detection by monitoring the adverse impact of Doppler bias on the PVT solution. The proposed Clock Drift Monitoring (CDM) technique exploits the user clock drift derived from Doppler positioning as a detection metric, instead of directly extracting the Doppler bias. Under normal conditions, where all GNSS signals are authentic, the clock drift reflects the stability of the user’s frequency source and remains stable. Under spoofing conditions, however, counterfeit signals introduce a consistent Doppler bias across all measurements, resulting in abnormal variations in the clock drift. A detector based on the Generalized Likelihood Ratio Test (GLRT) is developed to identify these variations. Field tests are conducted to validate the effectiveness of the CDM technique in real-world scenarios. The results demonstrate that CDM is a practical and flexible method for GNSS spoofing detection, providing a robust solution for autonomous vehicles to counter emerging cyber threats.