GNSS-induced Spoofing Detection Based on SQM Correlation

‍ ‍The originally designed Signal Quality Monitoring （SQM） technology for multipath detection has been effective in identifying spoofing attacks for the Global Navigation Satellite System （GNSS）. Traditional SQM techniques directly use amplitude metrics to monitor spoofing attacks， and they have simple structures and good feasibility. However， they are susceptible to noise， resulting in a significant decrease in detection performance. The improved SQM technique， incorporating the Moving Variance （MV） and Moving Average （MA）， reduces the impact of noise and enhances the spoofing detection capability of SQM. Nevertheless， these techniques do not consider the correlation among multiple satellites. Therefore， this paper proposes a novel enhanced SQM technique that utilizes the inter-satellite correlation to detect spoofing， making it applicable in various spoofing environments. Inducing spoofing interference is widely considered to simultaneously attack multiple satellites， and each satellite undergoes an induced process. Moreover， the induction process of each satellite exhibits similar characteristics， resulting in similar distortions in the autocorrelation function （ACF） of multiple satellite tracking loops. Consequently， the variations in the SQM amplitude among different satellites are similar. Owing to the improved reflection of similarity through metric correlation， this paper introduces the correlation （Corr） of SQM metrics among multiple satellites as a novel measure for detecting spoofing occurrences. First， this article proposes an algorithm that utilizes the similarity in the induced spoofing process among multiple satellites， instead of treating spoofing attacks solely from the perspective of a single satellite. Second， a theoretical analysis shows that the new metric follows a Gaussian distribution in the absence of spoofing. The Neyman-Pearson （NP） hypothesis testing method is used to determine the optimal detection threshold， which determines whether the target receiver is being spoofed. Finally， the spoofing detection capability of the new method is validated using the Texas Spoofing Test Battery （TEXBAT） and compared with the MV and MA of SQM. The results indicate that the SQM method based on Corr can detect GNSS-induced spoofing attacks more effectively.