## 1 Introduction
Pulse blanking performs well in sparse interference scenarios by setting an amplitude threshold to zero out signals exceeding the limit, offering low hardware implementation costs [7]. Adaptive filtering schemes, which include infinite impulse response (IIR) and finite impulse response (FIR) structures, leverage the recursive nature of IIR filters to achieve lower computational complexity and superior statistical properties. However, due to the temporal overlap between position ing and interference signals, this method may result in the loss of useful time-domain information from the navigation signal when facing certain types of interference [8]. Transform-domain methods project mixed signal data into the time-frequency or feature subspace by selecting reversible mappings that maximize signal-to-interference separability in the transform domain. These methods suppress various types of interference through techniques such as zeroing or subspace projection, without requiring additional hardware and while preserving the capability for signal reconstruction. Common transform techniques include short-time Fourier transform (STFT) [9; 10], wavelet transform (WT) [11; 12], and Karhunen-Loeve transform (KLT) [13; 14; 15]. Although transform-domain approaches enhance interference energy compression and separation via frequency domain, time-frequency domain, or orthogonal basis decomposition, their deployment in dynamic and complex environments faces several common limitations:  
1. Different interference types require specific configurations of window length, mother wavelet type, or energy thresholds for optimal suppression. When cross-domain changes occur--such as variations in interference power levels or channel fading--preset parameters struggle to adapt synchronously, leading to either insufficient suppression bandwidth (resulting in residual leakage) or excessive bandwidth that inadvertently degrades the desired signal [16; 17]. 2. Transform-domain separation typically assumes significant energy differences between the signal and interference along at least one feature dimension. When the legitimate signal and interference exhibit similar energy levels in localized time-frequency regions, overly high thresholds may cause signal loss, while low thresholds allow interference sidelobes to persist, making it difficult to maintain consistent separation using fixed thresholds or projection dimensions. 3. Conventional transform-domain methods primarily target energy-based interference and lack capabilities for spoofing feature recognition or alerting. Spoofed components may remain latent in the observed data after transform processing, rendering subsequent filters unaware of degraded signal integrity, thereby continuously injecting false position information into the navigation solution and accumulating systemic bias [18; 19]. To address these challenges, this paper proposes a classification-driven differentiated jamming suppression (CDDJ) method, which incorporates prior knowledge of known interference types and associated confidence scores into the suppression process [20]. First, the theoretical upper and lower bounds of the output carrier-to-noise ratio (\(C/N_{0 att}\)) under representative interference scenarios are derived, revealing variations in the distribution of anti-jamming efficiency (\(\Gamma\)) and establishing the performance limits of unified suppression algorithms. Next, a mapping relationship is constructed between known interference types and their corresponding dedicated suppression algorithms, and an anti-jamming decision-switching criterion is formulated based on signal quality evaluation metrics. Finally, for inputs with insufficient confidence, a lightweight OpenMax-Lite rejection layer is appended to the end of the diagnostic network. This layer estimates the probability of unknown interference via the tail distribution of the Weibull function, based on class center distances.