Dual-Branch Network with Frequency-Aware Dynamic Convolution and Transformer for Specific Emitter Identification

抄録

Specific emitter identification plays a crucial role in the field of information security. To improve identification performance in complex electromagnetic environments, this letter proposes a dual-branch network, FADC-Transformer, which combines frequency-aware dynamic convolution (FADC) and Transformer. It can adaptively fuse frequency-domain features from FADC and time-domain features from the Transformer. Specifically, FADC introduces a frequency-band attention mechanism and dynamic kernel generation, which can dynamically adjust convolutional kernel parameters according to the inputs, resulting in better robustness. Experimental results show that the accuracy of FADC is improved by 16% compared with static convolution, and the dual-branch structure significantly enhances identification performance.