A graphical way of continuous GNSS spoofing detection at the baseband signal level https://doi.org/10.33012/2023.18628

Global Navigation Satellite System (GNSS) spoofing is nowadays an emerging topic, especially in safety-critical applications. Power monitoring and signal quality monitoring (SQM) are effective ways to identify spoofing by monitoring the abnormal power and the distorted auto-correlation function (ACF). However, the matched power spoofing is difficult to detect, and the SQM technique is only effective in the spoofing drag-off stage. In response to a need for a single metric that combines power monitoring and ACF distortion monitoring, a graphical way of continuous GNSS spoofing detection is proposed in this paper. This paper defines a new metric called ACF similarity to characterize the power of the spoofing signal and the spoofer dragging process. Based on the image features extracted from the time-domain transient response of multiple correlators outputs, the proposed metric can track the ACF and power abnormalities in both spoofer drag-off and steady-state periods. Simulation results prove that the ACF similarity statistic follows Gaussian distribution. As such, a spoofing detection algorithm with an optimal threshold is developed based on the Neyman-Pearson theorem. The performance of the proposed detector has been verified by utilizing the Texas Spoofing Test Battery (TEXBAT) dataset. Results show that with 1% false alarm rate, the detection probability of the proposed detector achieves 100% in Scenario 5 and 87% in Scenario 3 in TEXBAT dataset.