## 1 Introduction  
Radar systems serve as the eyes and ears of modern armed forces, providing vital intelligence for situational awareness, target tracking, and threat detection. In electronic warfare, adversaries employ various jamming signals to degrade radar detection, tracking, and guidance capabilities [1, 2]. For example, in air defense systems, jamming can disrupt tracking of aircraft and missiles, making it difficult to detect and intercept threats. Shipborne radars for surface surveillance are also susceptible to jamming, which can cloak naval vessels and enable attacks [3, 4]. The emergence of Digital Radio Frequency Memory (DRFM) systems has ushered in a new era for active radar jamming techniques, enabling adversaries to generate various jamming modes and making the effective operation of radar systems increasingly challenging. By identifying the jamming type, radar can invoke tailored anti-jamming techniques like sidelobe cancellation, increased power, or frequency hopping to mitigate the jamming effects [5, 6]. Therefore, accurate recognition of jamming signals is an essential step for radar systems to take appropriate countermeasures and maintain operational effectiveness under jamming [7, 8, 9]. Various techniques have been developed for radar jamming recognition over the years. Earlier methods rely on expert-crafted features based on signal processing concepts like frequency entropy and peak-to-peak ratio [10]. These hand-engineered features provide interpretable insights into jamming characteristics. However, these hand-engineered features often fail to capture subtle jamming patterns, especially at low jamming-to-noise ratios (JNRs) [11].The predefined features lack sufficient representation power to discern fine-grained differences when the signal is corrupted by noise. With the rise of deep learning, artificial intelligence-based data-driven approaches using convolutional neural networks (CNNs) have achieved new benchmarks. By automatically learning features from data, CNNs can extract intricate discriminative patterns without expert domain knowledge. Methods based on time-frequency representations and complex CNNs can attain over 95% accuracy on measured data [12; 13; 14; 15]. Advanced networks incorporating long short-term memory units [16; 17; 18], attention mechanisms [19; 20], and pruning [21] further boost performance. For example, [PERSON] et al. combined Fisher pruning and complex networks to achieve excellent accuracy even on low-resource devices [22; 23; 24]. Recent works also apply deep learning to large-scale collected data, obtaining promising results in real-world jamming recognition [25]. However, despite remarkable recognition accuracy under high JNRs, existing methods still face difficulties in discerning jamming types under low JNRs like \(-18\) dB [26]. Adversarial attacks can also degrade CNN performance [27; 28; 29; 30], posing challenges for deep learning based recognition. Despite promising results, existing methods still face difficulties in jamming recognition under low JNRs. Most methods can achieve over 90% accuracy when JNR is above \(-6\) dB [31; 32; 33; 34]. However, their performance deteriorates rapidly at even lower JNRs. Low JNR poses great challenges for subtle feature extraction. First, convolutional kernels for extracting image features of the jamming signal tend to focus on local patterns, whereas attention mechanisms are better suited to capture global contextual features. Second, strong background noise can drown out parts of the meaningful signal, requiring active denoising interventions to reverse this effect. Robust feature learning and selection are thus needed to address the low JNR jamming recognition problem from both perspectives [35; 36]. Advanced network architectures and noise-robust feature extraction need to be explored to enable accurate jamming recognition in complex noisy environments. Tackling the low JNR challenge will greatly extend the capability of radar systems against jamming attacks.