首页|Automatic modulation recognition of radiation source signals based on two-dimensional data matrix and improved residual neural network
Automatic modulation recognition of radiation source signals based on two-dimensional data matrix and improved residual neural network
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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 pre-processing process.Second,the depthwise convolution and large-size convolutional kernels based re-sidual 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.
Automatic modulation recognitionRadiation source signalsTwo-dimensional data matrixResidual neural networkDepthwise convolution
Guanghua Yi、Xinhong Hao、Xiaopeng Yan、Jian Dai、Yangtian Liu、Yanwen Han
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Science and Technology on Electromechanical Dynamic Control Laboratory,School of Mechatronical Engineering,Beijing Institute of Technology,Beijing 100081,China
BIT Tangshan Research Institute,Beijing 100081,China
National Natural Science Foundation of ChinaChina Postdoctoral Science Foundation
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