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 emi...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.展开更多
对于非协作通信场景下辐射源识别(SEI)问题,基于人工射频指纹特征(Radio Frequency Fingerprints,RFF)的识别方式准确率不高,基于深度学习的方法又对训练数据量有过高的要求。为了克服该问题,提出一种结合了人工射频指纹特征的基于贝叶...对于非协作通信场景下辐射源识别(SEI)问题,基于人工射频指纹特征(Radio Frequency Fingerprints,RFF)的识别方式准确率不高,基于深度学习的方法又对训练数据量有过高的要求。为了克服该问题,提出一种结合了人工射频指纹特征的基于贝叶斯卷积神经网络(CNN)的半监督SEI算法,将一个回归拟合信号双谱的直方图特征的CNN嵌入一个SEI的贝叶斯CNN中,并通过基于模糊度的半监督学习方法进一步降低算法对标签训练集的依赖性。在模拟数据集和真实数据集中的实验结果表明,在标签训练集规模为500~4 500条数据时,提出的方法比端到端的卷积神经网络识别方法的识别率提高了5%~20%。展开更多
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)。
文摘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.
文摘对于非协作通信场景下辐射源识别(SEI)问题,基于人工射频指纹特征(Radio Frequency Fingerprints,RFF)的识别方式准确率不高,基于深度学习的方法又对训练数据量有过高的要求。为了克服该问题,提出一种结合了人工射频指纹特征的基于贝叶斯卷积神经网络(CNN)的半监督SEI算法,将一个回归拟合信号双谱的直方图特征的CNN嵌入一个SEI的贝叶斯CNN中,并通过基于模糊度的半监督学习方法进一步降低算法对标签训练集的依赖性。在模拟数据集和真实数据集中的实验结果表明,在标签训练集规模为500~4 500条数据时,提出的方法比端到端的卷积神经网络识别方法的识别率提高了5%~20%。
