Revealing Methods of GNSS Spoofing Mitigation Through Analyzing the Spoofing Impacts on Adaptively Robust Estimation-Based RTK/INS Tightly Coupled Integration

With the increasing complexity of electromagnetic environment and the continuous progress of electronic interference technology, Global Navigation Satellite System (GNSS) spoofing attacks have become a practical threat. As a result, the focus of GNSS improvement has shifted from improving accuracy to improving reliability. Combining an inertial navigation system (INS) is effective for spoofing detection and mitigation since INS can independently provide high frequency, high accuracy, and multicategory navigation information within a period of time. This article focuses on analyzing the impact of spoofing attacks on GNSS/INS integration and explores potential anti-spoofing methods. A Beidou Navigation Satellite System (BDS) real-time kinematic (RTK)/INS tightly coupled model that involves spoofing measurements is established. The article describes an adaptively robust Kalman filter (ARKF)-based spoofing detection and mitigation method. The impact of spoofing attacks on the adaptively robust estimation is theoretically assessed. A combined spoofing-resistant strategy of ARKF and partial ambiguity resolution (PAR) is proposed to improve the ability of state estimation under GNSS spoofing. Semi-physical and semi-simulation experiments are carried out to verify our analysis and approach, which shows that compared with using extended Kalman filter (EKF), the BDS RTK/INS tightly coupled integration using ARKF can suppress the effect of spoofing attacks. However, its performance is degraded under ramp spoofing attacks and long-term step spoofing attacks. Additionally, employing a PAR can reduce the effect of outliers on ambiguity resolution, improve the accuracy of state estimation under spoofing attacks, and further enhance the system's robustness under GNSS spoofing.