基于深度残差适配网络的通信辐射源个体识别

doi:10.12305/j.issn.1001-506X.2021.03.02

Abstract

Abstract:

In order to solve the problem that the traditional artificial feature extraction method is not robust enough and the deep learning method needs a large number of labeled target domain data, a communication transmitter individual identification method based on deep residual adaptation network is proposed. Applying deep learning technology to realize the transfer recognition from the source domain to the target domain only needs to train the labeled source domain data and the unlabeled target domain data. The original communication emitter signal is input into the network training after preprocessing. The distribution difference between the source domain and the target domain and the loss function of the network are taken as the optimization objectives, and the final model is obtained by iteration. The experimental results on the actual communication emitter data set show that the method is feasible and effective.

Key words: transmitter individual identification, deep learning, transfer learning, feature extraction

CLC Number:

TN911.7

Hao CHEN, Jun'an YANG, Hui LIU. Communication transmitter individual identification based on deep residual adaptation network[J]. Systems Engineering and Electronics, 2021, 43(3): 603-609.

Figures/Tables 10

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References 23

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信号参数	参数值
信号载频/MHz	450, 512
调制方式	8 PSK
信号带宽/MHz	2
采样率/MHz	3.84
信噪比/dB	大于15
信号长度	约等于8×10⁶

算法	f₀/MHz
算法	450→512	512→450
EMD	54.2	49.4
ITD	57.2	60.8
VMD	62.8	57.6
TCA_EMD	49.6	45.2
TCA_ITD	52.8	51.6
TCA_VMD	52.6	55.2
LSTM	24.4	20.8
ResNet	68.2	64.8
DRAN	97.6	91.8

算法	f₀/MHz
算法	450→512	512→450
DRAN(目标域无标签)	97.6	91.8
DRAN(目标域有标签)	99.8	99.6

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Communication transmitter individual identification based on deep residual adaptation network

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