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
Radio Frequency(RF) fingerprinting is one physical-layer authentication method for wireless communication, which uses the unique hardware characteristic of the transmitter to identify its true identity.To improve the ...Radio Frequency(RF) fingerprinting is one physical-layer authentication method for wireless communication, which uses the unique hardware characteristic of the transmitter to identify its true identity.To improve the performance of RF Fingerprint(RFF)based on preamble with fixed duration, a nonlinear RF fingerprinting method based on payload symbols is proposed for the wireless OFDM communication with the bit mapping scheme of QPSK. The wireless communication system is modeled as a Hammerstein system containing the nonlinear transmitter and multipath fading channel. A parameter separation technique based on orthogonal polynomial is presented for the estimation of the parameters of the Hammerstein system. The Hammerstein system parameter separation technique is firstly used to estimate the linear parameter with the training signal, which is used to compensate the adverse effect of the linear channel for the demodulation of the successive payload symbols. The demodulated payload symbols are further used to estimate the nonlinear coefficients of the transmitter with the Hammerstein system parameter separation technique again, which is used as the novel RFF for the authentication of the QPSK-OFDM device. Numerical simulations have verified the proposed method, which can also be extended to the OFDM signals with other bit mapping schemes.展开更多
基于深度学习的射频指纹(Radio Frequency Fingerprinting, RFF)识别具有增强物理层安全性能的潜力。近年来,为了满足深度学习对大规模数据的需求,提出了几种RFF数据集。然而,这些数据集是从类似的信道环境中收集的,多数仅提供来自接收...基于深度学习的射频指纹(Radio Frequency Fingerprinting, RFF)识别具有增强物理层安全性能的潜力。近年来,为了满足深度学习对大规模数据的需求,提出了几种RFF数据集。然而,这些数据集是从类似的信道环境中收集的,多数仅提供来自接收器的接收数据。针对上述问题,利用软件无线电设备作为无线电信号发生器,通过自定义收发射机系统参数,如频带、调制模式、天线增益等,实现射频信号数据集的个性化定制。由于数据集是通过各种复杂的信道环境生成的,旨在更好地描述现实世界中的射频信号,因此在发射机和接收机处同时收集数据,可以模拟基于长期演进(Long Term Evolution, LTE)的真实RFF数据集。此外,通过一个基于卷积神经网络的射频指纹识别例程,验证了数据集的可用性,所提出的数据集和相关代码均可以在GitHub下载。展开更多
传统的基于密码机制和安全协议的无线网络安全存在隐患,新的基于物理层的射频指纹(radio frequency fingerprinting,RFF)方法利用发射机射频信号的细微差异来区分不同个体,具有难以克隆、伪造的优点,有着广阔的应用前景.本文首先讨论了...传统的基于密码机制和安全协议的无线网络安全存在隐患,新的基于物理层的射频指纹(radio frequency fingerprinting,RFF)方法利用发射机射频信号的细微差异来区分不同个体,具有难以克隆、伪造的优点,有着广阔的应用前景.本文首先讨论了理想RFF应具备的四种基本特性,即唯一性、时不变性、独立性和稳健性,分析了在四种基本特性方面的研究现状.然后按照信号预处理、特征提取和分类识别三个部分,对RFF识别相关技术进行了总结,重点分析了射频独特原生属性(RF-distinct native attribute,RF-DNA)、调制域和基于深度学习的RFF识别方法.最后,对RFF识别研究中涉及到的各种信号类型/调制方式及对应的应用场景进行了总结,展示了RFF识别的广阔应用前景,并对RFF识别的研究趋势进行了讨论.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
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Radio Frequency(RF) fingerprinting is one physical-layer authentication method for wireless communication, which uses the unique hardware characteristic of the transmitter to identify its true identity.To improve the performance of RF Fingerprint(RFF)based on preamble with fixed duration, a nonlinear RF fingerprinting method based on payload symbols is proposed for the wireless OFDM communication with the bit mapping scheme of QPSK. The wireless communication system is modeled as a Hammerstein system containing the nonlinear transmitter and multipath fading channel. A parameter separation technique based on orthogonal polynomial is presented for the estimation of the parameters of the Hammerstein system. The Hammerstein system parameter separation technique is firstly used to estimate the linear parameter with the training signal, which is used to compensate the adverse effect of the linear channel for the demodulation of the successive payload symbols. The demodulated payload symbols are further used to estimate the nonlinear coefficients of the transmitter with the Hammerstein system parameter separation technique again, which is used as the novel RFF for the authentication of the QPSK-OFDM device. Numerical simulations have verified the proposed method, which can also be extended to the OFDM signals with other bit mapping schemes.
文摘基于深度学习的射频指纹(Radio Frequency Fingerprinting, RFF)识别具有增强物理层安全性能的潜力。近年来,为了满足深度学习对大规模数据的需求,提出了几种RFF数据集。然而,这些数据集是从类似的信道环境中收集的,多数仅提供来自接收器的接收数据。针对上述问题,利用软件无线电设备作为无线电信号发生器,通过自定义收发射机系统参数,如频带、调制模式、天线增益等,实现射频信号数据集的个性化定制。由于数据集是通过各种复杂的信道环境生成的,旨在更好地描述现实世界中的射频信号,因此在发射机和接收机处同时收集数据,可以模拟基于长期演进(Long Term Evolution, LTE)的真实RFF数据集。此外,通过一个基于卷积神经网络的射频指纹识别例程,验证了数据集的可用性,所提出的数据集和相关代码均可以在GitHub下载。
文摘传统的基于密码机制和安全协议的无线网络安全存在隐患,新的基于物理层的射频指纹(radio frequency fingerprinting,RFF)方法利用发射机射频信号的细微差异来区分不同个体,具有难以克隆、伪造的优点,有着广阔的应用前景.本文首先讨论了理想RFF应具备的四种基本特性,即唯一性、时不变性、独立性和稳健性,分析了在四种基本特性方面的研究现状.然后按照信号预处理、特征提取和分类识别三个部分,对RFF识别相关技术进行了总结,重点分析了射频独特原生属性(RF-distinct native attribute,RF-DNA)、调制域和基于深度学习的RFF识别方法.最后,对RFF识别研究中涉及到的各种信号类型/调制方式及对应的应用场景进行了总结,展示了RFF识别的广阔应用前景,并对RFF识别的研究趋势进行了讨论.
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.
