A method of personal identification of communication radiation source based on complex-valued residual network

In complex electromagnetic environment, a method of individual identification of communication emitter based on complex residual network was proposed in this paper. The aim was to solve the problems of poor classification effect of traditional feature extraction and low identification accuracy of real neural network in low SNR environment. In this paper, we first combined the data collected from the I-channel and Q-channel into a complex number as input, and selected the complex initialization method and activation function according to the characteristics of the radio data set. Then we constructed the complex-valued residual network based on the improved complex-valued residual block, and finally applied the further optimized network structure to the task of individual identification of communication emitter. Experimental results show that, the complex-valued residual network has better performance, compared with the real residual neural network and the artificial feature extraction method. Moreover, the method based on complex-valued residual network is more robust under the condition of low SNR.