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

作　　者：FAN Rong SI Chengke HAN Yi WAN Qun 

机构地区：[1]School of Information and Communication Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China [2]Institute of Electronic and Electrical Engineering,Civil Aviation Flight University of China,Guanghan 618307,China

出　　处：《Journal of Systems Engineering and Electronics》2024年第3期558-574,F0002,共18页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China(62061003);Sichuan Science and Technology Program(2021YFG0192);the Research Foundation of the Civil Aviation Flight University of China(ZJ2020-04,J2020-033)。

摘　　要：Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature 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 network,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 individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of identification.Moreover,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is improved.Finally,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction speed.Experimental results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.

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

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TN911.7[自动化与计算机技术—控制科学与工程]