Sparse Decomposition-Based Anti-Spoofing Framework for GNSS Receiver: Spoofing Detection, Classification, and Position Recovery

Abstract

Achieving reliable navigation is critical for GNSS receivers subject to spoofing attacks. Utilizing the inherent sparsity and inconsistency of spoofing signals, this paper proposes an anti-spoofing framework for GNSS receivers to detect, classify, and recover positions from spoofing attacks without additional devices. A sparse decomposition algorithm with non-negative constraints limited by signal power magnitudes is proposed to achieve accurate spoofing detections while extracting key features of the received signals. In the classification stage, these features continuously refine each channel of the receiver’s code tracking loop, ensuring that it tracks either the authentic or counterfeit signal components. Moreover, by leveraging the inherent inconsistency of spoofing properties, we incorporate the Hausdorff distance to determine the most overlapped position sets, distinguishing genuine trajectories and mitigating spoofing effects. Experiments on the TEXBAT dataset show that the proposed algorithm detects 98% of spoofing attacks, ensuring stable position recovery with an average RMSE of 6.32 m across various time periods.