一种有监督子域适应的辐射源个体识别方法OA

For the significant decline in identification accuracy of specific emitter identification(SEI)under channel interference,a supervised subdomain adaptation method is proposed.This method leverages subdomain adaptation to align feature distributions across different subdomains.Initially,a Fourier analysis network(FAN)is employed to replace the multilayer perceptron in a traditional convolutional neural network(CNN),resulting in the CNN-FAN model.This model directly extracts signal features from RAW I/Q signals.Subsequently,the extracted features are classified,and the local maximum mean discrepancy(LMMD)is computed.By iteratively optimizing network parameters to minimize classification loss and the LMMD,the model aligns the feature distributions of the same emitter under different channel conditions,thus enhancing SEI performance under channel interference.Experimental results demonstrate that the CNN-FAN achieves an identification accuracy of 97.08%under additive white Gaussian noise channel interference with a signal-to-noise ratio of 10 dB.Furthermore,the proposed supervised subdomain adaptation-based SEI method achieves identification accuracies of 99.17%,96.83%,and 93.83%,respectively,under three types of actual channel interference.