# An Accurate GNSS Spoofing Detection Method Based on Multiscale Eye Diagrams
## 1 Introduction  
Civilian global navigation satellite system (GNSS) frequencies are accessible to the general public and provide services for navigation, positioning, and timing under all weather conditions on a global scale [1]. These systems are becoming increasingly important in contemporary society. In the past, the primary concerns of users and developers were the availability and accuracy of these systems. However, with the advancement of this technology and the expansion of its application areas, security issues have begun to attract widespread attention and have become a key factor in improving system performance [2, 3]. The inherent weakness of GNSS signals renders them susceptible to radio frequency interference [4], particularly spoofing interference. This type of interference is particularly insidious because it can result in the transmission of spoofed GNSS signals, which can cause the receiver to output incorrect position, velocity, and time (PVT) information without theuser's knowledge [5; 6; 7], thereby seriously compromising user safety. It is therefore critical to improve the ability of GNSS receivers to detect spoofed signals to ensure user safety. To safeguard satellite signals, several spoofing detection methods have been devised. At the level of signal transmission, [PERSON] proposed a cryptographic authentication technique designed to thwart spoofing attacks [8]. This approach involves encrypting navigation signals and verifying the accuracy and integrity of the received signals. However, this cryptography-based detection method necessitates alterations to the satellite signal source, which is prohibitively expensive and challenging to implement in the near future, particularly in light of the constraints inherent to the existing signal system architecture. Research at the signal reception end has concentrated on analyzing the anomalous signal characteristics resulting from spoofing attacks. These studies can be classified into three categories. The first category employs spatial processing techniques utilizing a receiver antenna array [9; 10; 11], and these techniques have robust antispoofing capabilities. However, this approach is costly and necessitates the use of expensive antenna hardware. The second category comprises baseband signal processing techniques that operate within the receiver's tracking loop [12], including signal quality monitoring (SQM) [13], Doppler anomaly detection [14], and signal energy detection [15; 16; 17]. The third category includes the postprocessing of observational data after the receiver has been located [18]; such methods perform consistency checks on multiple sets of disparate data, such as ephemeris data and clock offset data detection. Among the abovementioned detection methods, the second type of signal reception processing technology is favored by many researchers because it does not require additional hardware, has low complexity and cost, and is easy to implement. In particular, the SQM metrics, a series of detection methods based on the output value of the correlator, which include Delta, Ratio, Slope, and ELP, can all be implemented in tracking the receiver [19; 20]. [PERSON] first proposed the two SQM metrics Delta and Ratio, which aim to identify signal abnormalities by detecting changes in the correlation peak function [21]. The Delta metric is mainly used to determine the symmetry on both sides of the correlation peak function, whereas the Ratio metric is used to evaluate the distortion at the top of the correlation peak function. However, when the code phase, carrier phase, and \(C/N_{0}\) are very close to those of the real signal, the change in the correlation peak may not be obvious [22], which greatly reduces the effectiveness of detection. [PERSON] proposed the use of the early-late phase (ELP) correlator of the tracking loop to identify spoofing signals [23].