# An Accurate GNSS Spoofing Detection Method Based on Multiscale Eye Diagrams
## 1 Introduction
Although the ELP can accurately detect small code phase differences, its detection performance significantly decreases when the carrier phase of the spoofing signal is close to an integral multiple of the true signal, in which case, the probability of detecting the spoofing signal is reduced almost to zero. [PERSON] determined the presence of spoofing signals by comparing the estimated slope of the correlation peak function with its expected value [13]. This method requires an additional correlator, and its effectiveness is strongly impacted by the position of the correlator. When the distortion of the correlator position is large, the detection performance is excellent, but, when the distortion is small, the effect is not good. [PERSON] and [PERSON] conducted a comparative analysis of the Slope metric against other metrics and validated their findings using the Texas Spoofing Test Battery (TEXBAT) dataset from the University of Texas [16; 24]. [PERSON] used a combination of two metrics to comprehensively evaluate the quality of the autocorrelation function [25]. [PERSON] introduced \(C/N_{0}\) as a parameter for detecting spoofing signals [26], which we refer to as the CN0 method hereafter. This method results in greatly reduced detection performance when the signal strength is low or when the spoofing signal increases the signal power and noise simultaneously. The eye diagram is a common tool used in the field of communications to assess the performance of digital signal transmission systems. It visually displays the time-domaincharacteristics and amplitude characteristics of the signal, especially the distortions and noise effects during signal transmission. The edge features of the eye diagram, such as the eye height, are a key part of understanding and analyzing signal integrity. This paper presents an eye diagram detection method based on a multiscale Canny algorithm with minimum misjudgment probability (EDDM-MSC-MMP). This approach aims to enhance the edge detection capability of the eye diagram and improve the detection of spoofing signals through eye diagram analysis. The method generates an eye diagram by extracting the pertinent values from the receiver and determines whether a spoofing signal is present by extracting the eye height and eye width data from the eye diagram via the multiscale Canny algorithm with minimum misjudgment probability (MSC-MMP). Compared with CN0-based methodologies, the EDDM-MSC-MMP exhibits superior detection efficacy in the context of spoofing attacks that do not alter the \(C/N_{0}\) value. During such attacks, the overall signal strength tends to increase, yet the \(C/N_{0}\) value may remain unaltered, thereby rendering CN0-based detection methods ineffective. The EDDM-MSC-MMP is capable of fully extracting the signal strength, effectively addressing scenarios in which the carrier power and noise power increase simultaneously, and overcoming the detection limitations of the CN0 method. In contrast to the signal quality monitoring method, the EDDM-MSC-MMP method does not rely on the distortion of the relevant peak function but rather directly observes the specific changes in the correlation value eye diagram to detect spoofing signals more effectively. The specific contributions of this paper are summarized as follows:  
1. We review the signal model of the receiver, explain the differences between real signals and spoofed signals, and discuss the impact of spoofing attacks on the signal. 2. We propose the eye diagram detection method based on a multiscale Canny algorithm with minimum misjudgment probability (EDDM-MSC-MMP). This method addresses the issue of low detection rates, or even the inability to detect spoofing attacks effectively, in certain spoofing scenarios encountered by traditional SQM and CN0 methods, providing a solution for spoofing detection. 3. We introduce the proposed MSC-MMP algorithm, which effectively suppresses interference from texture edges while maintaining edge integrity.