## 1 Introduction  
In complex urban electromagnetic environments, the dense deployment of electronic devices poses multiple interference threats to wireless positioning signals [1]. Given the considerable distance between navigation satellites and the Earth, directly jamming the satellites themselves is extremely difficult. Consequently, ground-based navigation receivers become the most vulnerable component in the entire system link [2]. Since navigation signals utilize publicly known frequencies and structures and are subject to stringent power constraints, they undergo significant attenuation during atmospheric propagation. The long transmission distance further weakens the signal, resulting in extremely low power levels at the receiver, which makes it relatively easy to disrupt. Taking the BeiDou Navigation Satellite System (BDS) as an example, the signal power in the B1 band at the receiver is typically around \(-160\) dBW. In more challenging environments, such as indoor spaces or urban canyons, the received signal strength may drop as low as \(-180\) dBW [3]. Previous studies have shown that a suppression-type global navigation satellite system (GNSS) jammer with only \(1\) watt of power can cause significant interference to navigation signals within a radius of approximately \(25\) km [4]. Therefore, enhancing the interference resilience of satellite navigation receivers in complex electromagnetic environments is critical for improving their anti-jamming performance and ensuring the availability of navigation services. The core of interference diagnosis lies in the real-time perception and accurate classification of interference events, serving as a decision-making foundation for anti-jamming strategies. Based on the observability of signal features, external interference can be categorized into map-observable and non-observable types. The former exhibits identifiable patterns in feature domains such as the time-frequency spectrum and the power spectrum, and typically includes suppression jamming (e.g., narrowband blocking), time-frequency domain spoofing (e.g., pseudocode delay/frequency shift modulation), and non-line-of-sight multipath interference. The latter encompasses highly covert mimicking spoofing and unmedicable signal disruptions, which lack distinguishable representations in conventional feature spaces. GNSS malicious interference generally includes suppression-type and deception-type interference. Regarding suppression interference, traditional interference suppression techniques primarily include spatial domain filtering, temporal domain filtering, and transform domain filtering. In recent years, researchers have applied spatial adaptive processing (SAP) technology to the field of GNSS receiver anti-interference. The core idea of this technology is to optimize the antenna pattern by adaptively adjusting the weighting coefficients of the array antenna, thereby filtering out interference signals in the spatial domain while maintaining high gain for the target signal. Classic spatial adaptive filtering methods primarily include the maximum signal-to-interference ratio (MSIR), minimum mean square error (MMSE), and minimum variance (MV) methods [5]. Among these, the minimum variance method is further subdivided into two categories: minimum variance distortionless response (MVDR) and power inversion (PI). Additionally, by introducing time-domain delay lines to extend spatial processing, the space-time adaptive processing (STAP) method is constructed, which further increases system degrees of freedom and enhances interference resistance [6]. However, the STAP method typically requires a large array size, increasing the antenna size of the receiver as well as manufacturing and maintenance costs, making it unsuitable for portable or device-size-constrained applications. Time-domain anti-jamming techniques suppress interference by exploiting the temporal characteristics distinguishing the positioning signal from jamming signals. These techniques are mainly categorized into two types.