Automatic modulation recognition of radiation source signals based on two-dimensional data matrix and improved residual neural network  

作　　者：Guanghua Yi Xinhong Hao Xiaopeng Yan Jian Dai Yangtian Liu Yanwen Han 

机构地区：[1]Science and Technology on Electromechanical Dynamic Control Laboratory,School of Mechatronical Engineering,Beijing Institute of Technology,Beijing 100081,China [2]BIT Tangshan Research Institute,Beijing 100081,China

出　　处：《Defence Technology（防务技术）》2024年第3期364-373,共10页Defence Technology

基　　金：National Natural Science Foundation of China under Grant No.61973037;China Postdoctoral Science Foundation under Grant No.2022M720419。

摘　　要：Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.

关 键 词：Automatic modulation recognition Radiation source signals Two-dimensional data matrix Residual neural network Depthwise convolution 

分 类 号：TN911.3[电子电信—通信与信息系统] TP183[电子电信—信息与通信工程]