## 1 Introduction  
Due to the rapid development of unmanned intelligent carriers, the utilisation of autonomous navigation systems has increased. Among them, GNSS/MIMU integrated navigation is most widely utilised because of its low cost and high accuracy. As a result of the open structure of satellite navigation signals with low ground signal power, easy interference could occur. Therefore, its security and reliability have become key research topics. With the development of global navigation satellite system (GNSS) interference technology, the means of interference have gradually evolved into more complex forms, including jamming and spoofing [1]. Spoofing can be subdivided into generative spoofing and forwarding spoofing. The rapid development of traction spoofing in recent years poses a significant risk to the normal operation of GNSS systems, which not only affects the execution of military missions but also threatens the security of various fields such as civil aviation equipment and intelligent driving [2, 3]. Although emerging technologies such as zeroing antennas have effectively mitigated the effects of traditional interference techniques [4, 5], GNSS spoofing attacks remain a major challenge to navigation security. Spoofing attacks mimic the strength and coding of real satellite signals, making it difficult to accurately distinguish real signals from spoofed signals via traditional signal differentiation methods [6]. To address this challenge, researchers have proposed a series of anti-spoofing techniques. Among them, navigation information encryption techniques integrate specific code sequences into extended code sequences and employ dedicated hardware and software modules for decoding and verification, significantly enhancing the authenticity check of satellite navigation signals [7, 8]. However, the effectiveness of these countermeasures remains limited because the nature of forwarding spoofing does not alter the navigation information. In signal quality monitoring (SQM) techniques, the interaction between spoofed signals and real satellite signals affects correlator outputs. By tracking and calculating correlator outputs, usually researchers can identify distortions in the correlation peaks, which typically manifest as asymmetry, unusual sharpness, or unusual flatness. Some researchers have proposed the method to detect spoofing through multi-correlator measurements of shape distortion [9; 10; 11; 12; 13]. This method compares measurements of its autocorrelation function with typical values and makes judgements based on noise variance thresholds. Unfortunately, this technique is limited by its use of a scalar tracking structure, which is ineffective in the absence of overlapping correlation peaks. [PERSON] et al. [14] addressed this limitation by introducing an SQM detection method based on a vector tracking structure. In this method, the received signals are jointly analysed even in the absence of overlapping correlation peaks, thereby facilitating spoofing detection. Nonetheless, accurate determination of the positions of spoofing signal sources remains challenging. Additionally, machine learning-based spoofing detection techniques have been increasingly applied for identifying spoofed signals. [PERSON] et al. explored the feasibility of applying generative adversarial networks (GANs) to spoofing detection and utilising their powerful generative and generalisation capabilities [15; 16]. Their experiments showed that the detection rate attains 98% when the pseudo-range code-phase difference between the spoofed and real signals exceeds 0.5 code slices [16]. [PERSON] et al. [17] proposed a multi-channel one-dimensional fully convolutional neural network (CNN) for spoofing detection. This method utilises the residual sequences of the system measurements to extract features at various scales, generating point-by-point decision-making results and yielding good detection and false alarm rates.