Communication transmitter individual identification based on deep residual adaptation network

被引：1
作者：
Chen H. [1 ]
Yang J. [1 ]
Liu H. [1 ]
机构：
[1] College of Electronic Countermeasures, National University of Defense Technology, Hefei
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2021年 / 43卷 / 03期
关键词：
Deep learning; Feature extraction; Transfer learning; Transmitter individual identification;
D O I：
10.12305/j.issn.1001-506X.2021.03.02
中图分类号：
学科分类号：
摘要：
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. © 2021, Editorial Office of Systems Engineering and Electronics. All right reserved.
引用
收藏
页码：603 / 609
页数：6
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