A GNSS/IMU Spoofing Detection Method Based on Motion Models for Vehicular Navigation

Abstract Given the wide application of inertial measurement units (IMUs) in vehicular navigation, IMU-aided global navigation satellite system (GNSS) spoofing detection methods have gained significant attention. One of the most effective detection methods utilizes IMU and GNSS measurements to achieve velocity increment direct comparison (VIDC). However, this intuitive approach shows unsatisfactory performance when facing concealed spoofing deviations and low-cost vehicle-mounted IMUs. To address this issue, we propose a motion-model-based (MM-based) VIDC spoofing detection method by exploiting vehicular kinematic characteristics to enhance the detection performance. The proposed method utilizes motion models to impose physically interpretable and low-dimensional constraints on velocity increment deviations, decreasing the uncertainty in detection. This paper elaborates on the principle of this method, derives the theoretical performance of different-order MM-based methods, and describes the relationship between orders of the optimal detection model and spoofing deviation. Simulations and field experiments demonstrate that the MM-based method achieves an approximately 30% higher detection rate than the conventional approach.