Dual-branch cross-attention network with concatenation-based selective fusion for composite radar jamming recognition in low jamming-to-noise ratio

Reliable recognition of composite radar jamming signals under severe noise conditions remains a critical challenge in modern electronic warfare. To address the feature drowning issue in low jamming-to-noise ratio (JNR) environments, we propose a deep learning-based dual-branch network that integrates a shifted-window Transformer branch and a convolutional branch (Swin-Conv) through a concatenation-based selective kernel (CSK) fusion module, termed SC-CSKNet. The proposed network jointly explores the local transient structures and global modulation dependencies of the jamming signal. Moreover, a residual bi-directional cross-attention module is designed for feature alignment, while a concatenation-based selective kernel fusion (CSK-fusion) module is used as an adaptive gate to dynamically suppress the noise-corrupted channels. Extensive experiments demonstrate that SC-CSKNet achieves a superior average recognition accuracy of 72.65% even in the low-JNR region and outperforms the best baseline ConvNeXt Tiny by 2.32%, while maintaining excellent computational efficiency.