Abstract
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.
基金项目
国家自然科学基金(62061003)
四川省科技计划(2021YFG0192)
Research Foundation of the Civil Aviation Flight University of China(ZJ2020-04J2020-033)
NETL
NSTL
万方数据