防务技术2024，Vol.33Issue(3) ：364-373.DOI:10.1016/j.dt.2023.07.004

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

Guanghua Yi Xinhong Hao Xiaopeng Yan Jian Dai Yangtian Liu Yanwen Han
防务技术2024，Vol.33Issue(3) ：364-373.DOI:10.1016/j.dt.2023.07.004
✕
  • 维普
  • 万方数据

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

Guanghua Yi 1Xinhong Hao 2Xiaopeng Yan 2Jian Dai 1Yangtian Liu 1Yanwen Han1
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作者信息

  • 1. Science and Technology on Electromechanical Dynamic Control Laboratory,School of Mechatronical Engineering,Beijing Institute of Technology,Beijing 100081,China
  • 2. 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
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Abstract

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.

Key words

Automatic modulation recognition/Radiation source signals/Two-dimensional data matrix/Residual neural network/Depthwise convolution

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基金项目

National Natural Science Foundation of China(61973037)

China Postdoctoral Science Foundation(2022M720419)

出版年

2024
防务技术
中国兵工学会

防务技术

CSTPCD
影响因子：0.358
ISSN：2214-9147
参考文献量34
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