Prior-guided Lightweight Noise-robust Multi-label Radar Composite Interference Recognition Network

Interference identification is a critical component in enhancing the anti-jamming capability of radar target recognition systems. Compared with single-type interference, composite interference poses substantially greater identification challenges due to its structural complexity and flexible combination patterns. However, most existing identification methods are purely data-driven and fail to incorporate interference prior knowledge, resulting in performance bottlenecks in complex scenarios and limited interpretability. Moreover, many approaches lack effective noise suppression mechanisms and are prone to noise overfitting under low Signal-to-Noise Ratio (SNR) conditions. To address these limitations, this study proposes a prior-guided, noise-robust multi-label recognition network for radar composite interference, which exploits time-domain symmetry priors in different interference types. First, a coarse-to-fine denoising strategy is employed to suppress noise while preserving and enhancing their prior structural characteristics, thereby alleviating noise-induced overfitting during the recognition process. Second, an autocorrelation-based symmetry score is introduced to quantify the strength of the interference prior. The score is then mapped into a gating mechanism via a symmetry encoder to guide interference feature fusion and temporal representation learning. Finally, noise intensity and temporal features are jointly embedded into the recognition network, further enhancing the robustness of the proposed method across varying SNR conditions. Experimental results demonstrate that, under low-SNR conditions, the proposed method achieves average recognition accuracies exceeding 90% for 15 types of intermittent sampling repeater composite interference and 30 types of complex composite interference. Moreover, the proposed approach outperforms the strongest baseline model while significantly reducing model parameters.