This study presents a radio frequency(RF)fingerprint identification method combining a convolutional neural network(CNN)and gated recurrent unit(GRU)network to identify measurement and control signals.The proposed alg...This study presents a radio frequency(RF)fingerprint identification method combining a convolutional neural network(CNN)and gated recurrent unit(GRU)network to identify measurement and control signals.The proposed algorithm(CNN-GRU)uses a convolutional layer to extract the IQ-related learning timing features.A GRU network extracts timing features at a deeper level before outputting the final identification results.The number of parameters and the algorithm’s complexity are reduced by optimizing the convolutional layer structure and replacing multiple fully-connected layers with gated cyclic units.Simulation experiments show that the algorithm achieves an average identification accuracy of 84.74% at a -10 dB to 20 dB signal-to-noise ratio(SNR)with fewer parameters and less computation than a network model with the same identification rate in a software radio dataset containing multiple USRP X310s from the same manufacturer,with fewer parameters and less computation than a network model with the same identification rate.The algorithm is used to identify measurement and control signals and ensure the security of the measurement and control link with theoretical and engineering applications.展开更多
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
针对复杂的室内环境下,传统的射频识别技术(radio frequency identification,RFID)室内定位技术获得的接收信号强度特征向量维数较低,不能充分描述环境信息,无法获得良好的定位效果的问题,基于联合指纹提出一种鲁棒性强的高精度室内定...针对复杂的室内环境下,传统的射频识别技术(radio frequency identification,RFID)室内定位技术获得的接收信号强度特征向量维数较低,不能充分描述环境信息,无法获得良好的定位效果的问题,基于联合指纹提出一种鲁棒性强的高精度室内定位算法。该算法首先从RFID阅读器接收到的信号中提取信号强度和相位差数据,建立指纹库。然后利用凹函数递减策略改进PSO算法,优化SVR模型训练样本数据,建立参考标签的指纹特征和其与阅读器距离的映射关系。最后利用改进PSO算法迭代寻优,从而提高室内定位精度和鲁棒性。在仿真中,将该算法与GA-SVR和PSO-SVR算法进行比较,分析了不同指纹数据集和噪声对定位性能的影响。仿真结果表明,在相同指纹数据集和环境下,该算法的定位精度和系统稳定性均优于其他两种算法。展开更多
在射频指纹(radio frequency fingerprint,RFF)识别系统中,考虑到同一发射机的鲁棒性与不同发射机之间的差异性,提出了将瞬态信号二阶谱中的功率谱密度和互功率谱密度两个特征融合作为指纹的方法,并结合径向基概率神经网络分类器来进行...在射频指纹(radio frequency fingerprint,RFF)识别系统中,考虑到同一发射机的鲁棒性与不同发射机之间的差异性,提出了将瞬态信号二阶谱中的功率谱密度和互功率谱密度两个特征融合作为指纹的方法,并结合径向基概率神经网络分类器来进行分类.同时,对同一型号两个系列的多种无线网卡进行了分类检测,并与不同的特征提取方法和分类器进行了比较.结果表明,与已有方法相比,此方法的分类精确度有较大的提高.展开更多
在分析现有基于(EPC Class 1Gen,2EPCGen2)标准的轻量级RFID相互认证协议的基础上,提出了一种符合EPCGen2标准的基于射频指纹的RFID认证协议。协议融合了RFID设备的物理层信息,实现了RFID标签的跨层融合认证,具有增强RFID系统安全强度...在分析现有基于(EPC Class 1Gen,2EPCGen2)标准的轻量级RFID相互认证协议的基础上,提出了一种符合EPCGen2标准的基于射频指纹的RFID认证协议。协议融合了RFID设备的物理层信息,实现了RFID标签的跨层融合认证,具有增强RFID系统安全强度的特点。分析显示,提出协议具有相互认证、私密性、防止重放攻击、防止去同步攻击等安全性能,尤其能有效对抗RFID标签的克隆攻击。展开更多
基金supported by the National Natural Science Foundation of China(No.62027801).
文摘This study presents a radio frequency(RF)fingerprint identification method combining a convolutional neural network(CNN)and gated recurrent unit(GRU)network to identify measurement and control signals.The proposed algorithm(CNN-GRU)uses a convolutional layer to extract the IQ-related learning timing features.A GRU network extracts timing features at a deeper level before outputting the final identification results.The number of parameters and the algorithm’s complexity are reduced by optimizing the convolutional layer structure and replacing multiple fully-connected layers with gated cyclic units.Simulation experiments show that the algorithm achieves an average identification accuracy of 84.74% at a -10 dB to 20 dB signal-to-noise ratio(SNR)with fewer parameters and less computation than a network model with the same identification rate in a software radio dataset containing multiple USRP X310s from the same manufacturer,with fewer parameters and less computation than a network model with the same identification rate.The algorithm is used to identify measurement and control signals and ensure the security of the measurement and control link with theoretical and engineering applications.
基金supported by the National Natural Science Foundation of China(62061003)Sichuan Science and Technology Program(2021YFG0192)+1 种基金the Research Foundation of the Civil Aviation Flight University of China(ZJ2020-04J2020-033)。
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.
文摘针对复杂的室内环境下,传统的射频识别技术(radio frequency identification,RFID)室内定位技术获得的接收信号强度特征向量维数较低,不能充分描述环境信息,无法获得良好的定位效果的问题,基于联合指纹提出一种鲁棒性强的高精度室内定位算法。该算法首先从RFID阅读器接收到的信号中提取信号强度和相位差数据,建立指纹库。然后利用凹函数递减策略改进PSO算法,优化SVR模型训练样本数据,建立参考标签的指纹特征和其与阅读器距离的映射关系。最后利用改进PSO算法迭代寻优,从而提高室内定位精度和鲁棒性。在仿真中,将该算法与GA-SVR和PSO-SVR算法进行比较,分析了不同指纹数据集和噪声对定位性能的影响。仿真结果表明,在相同指纹数据集和环境下,该算法的定位精度和系统稳定性均优于其他两种算法。
文摘在射频指纹(radio frequency fingerprint,RFF)识别系统中,考虑到同一发射机的鲁棒性与不同发射机之间的差异性,提出了将瞬态信号二阶谱中的功率谱密度和互功率谱密度两个特征融合作为指纹的方法,并结合径向基概率神经网络分类器来进行分类.同时,对同一型号两个系列的多种无线网卡进行了分类检测,并与不同的特征提取方法和分类器进行了比较.结果表明,与已有方法相比,此方法的分类精确度有较大的提高.
文摘在分析现有基于(EPC Class 1Gen,2EPCGen2)标准的轻量级RFID相互认证协议的基础上,提出了一种符合EPCGen2标准的基于射频指纹的RFID认证协议。协议融合了RFID设备的物理层信息,实现了RFID标签的跨层融合认证,具有增强RFID系统安全强度的特点。分析显示,提出协议具有相互认证、私密性、防止重放攻击、防止去同步攻击等安全性能,尤其能有效对抗RFID标签的克隆攻击。
