Detection of GNSS spoofed signals based on the weighted moving average bias correction method

Spoofing intrusions pose a major threat to user security by delivering incorrect information. The detection rate of existing signal quality monitoring (SQM) metrics notably decreases when faced with numerous specific combinations of code phases and carrier phases in spoofing signal instances. To increase the detection rate and coverage, we exploit the offset detection capability of different correlators and propose metrics: multipoint slope differential (MuSD) and multipoint slope differential averaging (MuSDA). In addition, this paper proposes a Weighted Moving Average Bias Correction (WMA-BC) algorithm for metric post-processing. Comparative experiments with Moving Average (MA) and Moving Variance (MV) based SQM methods demonstrate that the WMA-BC algorithm achieves substantial advantages in Detection Rate enhancement and significantly improves the Area Under Curve (AUC) of the Receiver Operating Characteristic (ROC). In experiments with different code and carrier phase offsets, the detection coverage of the MuSD and MuSDA metrics reached 96.1% and 95.8%, respectively, which are much greater than those of other metrics. From the detection rates obtained in seven spoofing intrusion experiments based on the Texas Spoofing Test Battery (TEXBAT) dataset collected at the University of Texas, the proposed MuSDA and MuSD metrics outperform other metrics by approximately 11% to 97%.