A post-correlation graphical way for continuous GNSS spoofing detection

Global navigation satellite system (GNSS) receivers are under the threat of spoofing attacks. The signal quality monitoring (SQM) technique is effective in monitoring the distorted auto-correlation function (ACF). However, it has limited detection effectiveness in detecting spoofing signals that are greatly overpowered and with signifi-cant code phase shifts. This paper introduces a new metric called ACF similarity to characterize the ACF distortion and power abnormality in the tracking loop based on image feature extraction techniques. The spoofing detection algorithm and the optimal threshold utilizing the generalized likelihood ratio test (GLRT) are further developed. The performance of the proposed detector has been verified by utilizing the Texas Spoofing Test Battery (TEXBAT) dataset and Monte Carlo simulation tests. Results show that with 1% false alarm rate, the proposed detector achieves a detection probability of 87% in the TEXBAT datasets and provides better detection sensitivity than the conventional SQM metrics.

Automated summary

This paper introduces a new metric called ACF similarity to characterize the distortion and abnormality of the auto-correlation function (ACF) in GNSS receivers. The proposed spoofing detection algorithm based on image feature extraction techniques achieves a detection probability of 87% with a 1% false alarm rate, showing better sensitivity than the conventional SQM metrics.