# An Accurate GNSS Spoofing Detection Method Based on Multiscale Eye Diagrams
## 6 Conclusions  
In this study, we propose an MSC-MMP algorithm for extracting the edges of an eye diagram. On this basis, we propose the EDDM-MSC-MMP to detect spoofing signals. Through theoretical analysis, simulation, and experimentation, we draw the following conclusions:  
1. The MSC-MMP algorithm significantly improves the accuracy of eye diagram edge detection. In particular, the algorithm effectively suppresses the interference of texture edges while maintaining the integrity of the edges. It solves the problems of the difficulty of detecting fine texture edges and inaccurate edge positioning when large Gaussian windows are used, and it avoids retaining too many texture edges when small windows are used. Tests on the eye diagram dataset that we constructed show that the MSC-MMP algorithm achieves an optimal balance between accuracy and recall, achieving an ODS-F score of 0.970, which significantly exceeds the Canny algorithm's score of 0.898 and the Log and Zero Crossing algorithm's score of 0.852. These results demonstrate the advantages and potential of the MSC-MMP algorithm for eye diagram edge detection. The EDDM-MSC-MMP is a highly sensitive spoofing detection algorithm with high detection capability and excellent detection coverage. Among a large number of algorithms, the EDDM-MSC-MMP demonstrated the highest stability, with a detection coverage of over 95% under different minimum acceptable detection rate requirements (from 95% to 30%). Even with a minimum acceptable detection rate of 95%, the detection coverage of the EDDM-MSC-MMP still reached 95.35%, which is significantly higher than the highest coverage of 1.02% for the SQM (including the composite metrics Ratio+ELP and Slope+Delta) and 0.75% for the CN0 method, and it is clearly superior to the competing algorithms. 3. The EDDM-MSC-MMP method has an excellent ROC curve and overall performance in a realistic complex environment. On the real TEXBAT spoofing dataset, its AUC value was significantly higher than those of the other detection techniques, reaching 0.971, 0.994, and 0.947 in three typical cases. In contrast, the AUC values of the other methods ranged from 0.65 to 0.92. In the eight cases in the dataset, the EDDM-MSC-MMP consistently achieved the highest detection results, outperforming the other algorithms by 5% to 98%. In particular, the EDDM-MSC-MMP was able to achieve a detection rate of up to 98.11% even when the other methods performed poorly, as in Case 5. This consistent outstanding performance highlights the reliability and efficiency of this method in complex signal environments. Conceptualization, C.W. ; methodology, C.W. ; software, X.S. ; validation, Y.J. ; formal analysis, C.W. ; investigation, [PERSON]; resources, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]; visualization, [PERSON]; supervision, X.S. ; project administration, X.S. ; funding acquisition, Y.J. All authors have read and agreed to the published version of the manuscript. This research was supported by the National Natural Science Foundation of China (Grant No. U23A20280, Grant No. 62161007, Grant No. 62471153), Guangxi Science and Technology Project (GuikeZY23055048, GuikeAA23062038, GuikeAB23026120, GuikeZY22096026, GuilikeAA24206043), the Natural Science Foundation of Guangxi (Grant No. 2024 GXNSFBA010265, Grant No. 2024 GXNSFBA010265, Grant No. 2024 GXNSFBA999284, Grant No. 2024 GXNSFBA999304), the Guangxi Major Talent Project, the Graduate Student Innovation Project of Guilin University of Electronic Science and Technology, and the Project of the Collaborative Innovation Center for BeiDou Location Service and Border and Coastal Defense Security Application.