An Adaptive Specific Emitter Identification System for Dynamic Noise Domain

With the sharply increasing number of wireless devices, specific emitter identification (SEI) technology based on radio frequency fingerprint (RFF) is employed to enhance devices’ security for authentication. However, the problems of poor performance in a highly dynamic interference environment, high dependence on quality of data sets, and less consideration of calculation resources are urgent to be resolved in a practical SEI applying domain. Our adaptive SEI system focuses on these problems, proposing a preprocessing algorithm improving synchrosqueezed wavelet transforms by energy regularization (ISWTE) to simplify the process of signal preprocessing while improving the robustness of RFF, an unsupervised neural network noise feature extracting GAN (NEGAN) to reduce the dependence of the data sets quality while obtaining precise clean RFF features from signals with noises and an optimized structure of waveform and classifier to achieve a better performance with less complexity and little cost. To test the system performance, we have generated a real wireless device data sets with ten devices, including a training set and a base test set both with SNR 10 dB, and a dynamic test set with SNR ranges −20 to 10 dB. The proposed adaptive SEI system by our work obtains a test accuracy of 0.96 at SNR 10 dB, 0.85 at SNR 0 dB, and 0.25 at SNR −20 dB only through SNR 10-dB training set. Most of the existing methods through neither of high SNR or enhanced mixed SNRs training set failed to complete devices identification under SNR −10 dB, and test accuracy of them deceased dramatically at SNR 0 dB.