Channel-Robust Specific Emitter Identification Based on Domain-Adversarial Training of Neural Networks and Multi-Feature Fusion

To address the significant decline in the accuracy of Specific Emitter Identification(SEI) under wireless channel, we propose a novel method that combines a domain adversarial network with multi-feature fusion(MFF) to extract domain-invariant features of the signal and leverage the complementary nature of signal features extracted from different views. Initially, we employ the IQ Convolutional Neural Network (IQCNN), the Gate Recurrent Unit (GRU), and the stacked Fourier Analysis Networks (SFAN) to directly extract and fuse correlation, temporal, and periodic features from the raw I/Q data. Subsequently, we integrate a Domain-Adversarial Training of Neural Networks (DANN) to eliminate channel features, ultimately enabling SEI under channel interference. The experimental results on the WiFi dataset demonstrate that the MFF network designed in this study achieves an identification accuracy of 97% under Additive White Gaussian Noise(AWGN) channel interference with a signal-to-noise ratio(SNR) of 10dB. Furthermore, the proposed method achieves identification accuracy of 93.8%, 90.3%, and 78.2% under three complex real-world channel interference scenarios, respectively. These findings indicate that the proposed method effectively mitigates channel interference and significantly enhances the robustness of SEI.