首页|RFFsNet-SEI:a multidimensional balanced-RFFs deep neural network framework for specific emitter identification

RFFsNet-SEI:a multidimensional balanced-RFFs deep neural network framework for specific emitter identification

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Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing fea-ture information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of identification.In this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,RFFsNet-SEI.Particularly,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train RFFsNet-SEI.As introducing model-aided RFFs into neural net-work,the hybrid-driven scheme including physical features and I-Q data is constructed.It improves physical interpretability of RFFsNet-SEI.Meanwhile,since RFFsNet-SEI identifies indivi-dual of emitters from received raw data in end-to-end,it accele-rates SEI implementation and simplifies procedures of identifica-tion.Moreover,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identifi-cation accuracy is improved.Finally,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,compu-tational complexity,and prediction speed.Experimental results illustrate that the proposed method outperforms the counter-parts on the basis of simulation dataset and real dataset col-lected in the anechoic chamber.

specific emitter identification(SEI)deep learning(DL)radio frequency fingerprint(RFF)multidimensional feature extraction(MFE)variational mode decomposition(VMD)

FAN Rong、SI Chengke、HAN Yi、WAN Qun

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School of Information and Communication Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China

Institute of Electronic and Electrical Engineering,Civil Aviation Flight University of China,Guanghan 618307,China

国家自然科学基金四川省科技计划Research Foundation of the Civil Aviation Flight University of China

620610032021YFG0192ZJ2020-04J2020-033

2024

10.23919/JSEE.2023.000069

系统工程与电子技术(英文版)
中国航天科工防御技术研究院 中国宇航学会 中国系统工程学会 中国系统仿真学会

系统工程与电子技术(英文版)

CSTPCD
影响因子：0.64
ISSN：1004-4132
年,卷(期)：2024.35(3)
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