D$^{2}$AF-Net: A Dual-Domain Adaptive Fusion Method for Radar Deception Jamming Recognition
Wen Zheng, Junpeng Shi, Yang Li, Zhitao Huang, Zhiyuan Zhang, Zhihui Li · IEEE Transactions on Aerospace and Electronic Systems · 2025
Deception jamming has garnered widespread attention for threatening radar systems due to its lower energy requirement compared to traditional suppression jamming. With the rapid evolution of electronic warfare technologies, multiple-input multiple-output (MIMO) radar has demonstrated remarkable progress in countering jamming. Non-networked deception jammers, capable of generating compound deception jamming within MIMO radar's main lobe, present significant challenges. However, existing jamming recognition methods suffer from static assumptions tailored to limited jamming patterns, excessive dependence on prior knowledge, inefficacy in extracting discriminative features, and high computational cost. To overcome these limitations, we first propose the dual-domain adaptive fusion network (D$^{2}$AF-Net). This innovative approach employs hierarchical and integrated features to alleviate recognition degradation and sensitivity to array amplitude and phase errors. Specifically, D$^{2}$AF-Net contains two fusion-based building blocks: an intra-domain Fusion module mixing local feature and global information, and a staged fusion strategy supporting inter-domain streaming feature interactions and reducing redundant features. Our extensive experimental results based on both simulation and semi-physical datasets show that D$^{2}$AF-Net achieves state-of-the-art performance on recognition rate, generalization ability in low JNR, and robustness for array errors while requiring fewer or comparable data processing time and interference time than competitive models.