A DFT-Based SQM Method for GNSS Spoofing Detection

Global Navigation Satellite Systems (GNSS) are vulnerable to spoofing attacks due to their inherent weaknesses. While conventional signal quality monitoring (SQM) methods such as classic SQM methods(SQM_Raw)using raw SQM metrics(Delta/Ratio/ELP), moving average based method(SQM_MA), and moving variance-based (SQM_MV ), based on I/Q correlator outputs can identify spoofing during the pull-off stage, their performance is suboptimal—especially in multipath environments, where effectiveness deteriorates significantly or even fails entirely. To address this challenge, this paper proposes a normalized discrete fourier transform (DFT) based signal quality monitoring method(SQM_NDFT)designed for spoofing detection. By applying DFT normalization to conventional SQM metrics(Delta/Ratio/ELP), the method significantly enhances discrimination capability against spoofing attacks without frequency locking. Experiments on open-source datasets and self-collected experimental data demonstrate the method's superior performance especially in dynamic scenarios with multipath interference, where traditional approaches degrade.Delta_NDFT, Ratio_NDFT,andELP_NDFTdemonstrate greater than 92% detection probability at approximately 1% false alarm rate in static overpowered & frequency-unlocked scenario. Besides, the proposed method combines low computational complexity with hardware compatibility, making it highly suitable for real-world engineering applications.