Compound Jamming Recognition Under Low JNR Setting Based on a Dual-Branch Residual Fusion Network

In complex electromagnetic environments, radar systems face increasing challenges from advanced jamming techniques. These challenges mainly stem from the diversity of jamming patterns, the complexity of compound jamming signals, and the difficulty of recognition under low jamming-to-noise ratio conditions. Accurate recognition of such signals is critical for enhancing radar anti-jamming capabilities. However, traditional methods often struggle with diverse and evolving jamming patterns. To address this issue, we propose a novel deep learning-based approach for accurate and robust recognition of complex radar jamming signals. Specifically, the proposed network adopts a dual-branch architecture that concurrently processes time-domain and time-frequency-domain features of jamming signals. It further incorporates a multi-branch convolutional structure to strengthen feature extraction and applies an effective feature fusion strategy to capture subtle patterns. Simulation results demonstrate that the proposed method outperforms six representative baseline approaches in recognition accuracy and noise robustness, particularly under low jamming-to-noise ratio conditions.