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
辐射源系统特有的非线性可用于辐射源指纹识别(radio frequency fingerprinting,RFF)。特别地,基于非线性动力学重构相空间(reconstructed phase space,RPS)的特征对线性信道具有天然优势,且对细微差异更加敏感。然而,该方法在非理想场...辐射源系统特有的非线性可用于辐射源指纹识别(radio frequency fingerprinting,RFF)。特别地,基于非线性动力学重构相空间(reconstructed phase space,RPS)的特征对线性信道具有天然优势,且对细微差异更加敏感。然而,该方法在非理想场景中同样面临着鲁棒性不足的问题。为此,分析非线性动力学基础与其对应的RFF机理,结合实际非理想应用场景完善了相关理论模型;在此基础上,构造相空间K阶状态转移矩阵,提出通过表征K阶状态转移矩阵的特性来提取RFF特征的方法,并通过理论证明其鲁棒性。基于多种实测和仿真数据在随机扰动、多径衰落等场景下进行细致实验,结果表明所提方法特征机理清晰、计算简单,在多种场景下均表现出明显的鲁棒性优势,具有较高的应用价值。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
Radio frequency fingerprinting(RFF)is a technology that identifies the specific emitter of a received electromagnetic signal by external measurement of the minuscule hardware-level,device-specific imperfections.The RF...Radio frequency fingerprinting(RFF)is a technology that identifies the specific emitter of a received electromagnetic signal by external measurement of the minuscule hardware-level,device-specific imperfections.The RFF-related information is mainly in the form of unintentional modulation(UIM),which is subtle enough to be effectively imperceptible and is submerged in the intentional modulation(IM).It is necessary to minimize the influence of the IM and expand the slight differences between emitters for successful RFF.This paper proposes a UIM microstructure enlargement(UMME)method based on feature-level adaptive signal decomposition(ASD),accompanied by autocorrelation and cross-correlation analysis.The common IM part is evaluated by analyzing a newly-defined benchmark feature.Three different indexes are used to quantify the similarity,distance,and dependency of the RFF features from different devices.Experiments are conducted based on the real-world signals transmitted from 20 of the same type of radar in the same working mode.The visual image qualitatively shows the magnification of feature differences;different indicators quantitatively describe the changes in features.Compared with the original RFF feature,recognition results based on the Gaussian mixture model(GMM)classifier further validate the effectiveness of the proposed algorithm.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.
文摘辐射源系统特有的非线性可用于辐射源指纹识别(radio frequency fingerprinting,RFF)。特别地,基于非线性动力学重构相空间(reconstructed phase space,RPS)的特征对线性信道具有天然优势,且对细微差异更加敏感。然而,该方法在非理想场景中同样面临着鲁棒性不足的问题。为此,分析非线性动力学基础与其对应的RFF机理,结合实际非理想应用场景完善了相关理论模型;在此基础上,构造相空间K阶状态转移矩阵,提出通过表征K阶状态转移矩阵的特性来提取RFF特征的方法,并通过理论证明其鲁棒性。基于多种实测和仿真数据在随机扰动、多径衰落等场景下进行细致实验,结果表明所提方法特征机理清晰、计算简单,在多种场景下均表现出明显的鲁棒性优势,具有较高的应用价值。
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.
基金This work was supported by the Program for Innovative Research Groups of the Hunan Provincial Natural Science Foundation of China(2019JJ10004).
文摘Radio frequency fingerprinting(RFF)is a technology that identifies the specific emitter of a received electromagnetic signal by external measurement of the minuscule hardware-level,device-specific imperfections.The RFF-related information is mainly in the form of unintentional modulation(UIM),which is subtle enough to be effectively imperceptible and is submerged in the intentional modulation(IM).It is necessary to minimize the influence of the IM and expand the slight differences between emitters for successful RFF.This paper proposes a UIM microstructure enlargement(UMME)method based on feature-level adaptive signal decomposition(ASD),accompanied by autocorrelation and cross-correlation analysis.The common IM part is evaluated by analyzing a newly-defined benchmark feature.Three different indexes are used to quantify the similarity,distance,and dependency of the RFF features from different devices.Experiments are conducted based on the real-world signals transmitted from 20 of the same type of radar in the same working mode.The visual image qualitatively shows the magnification of feature differences;different indicators quantitatively describe the changes in features.Compared with the original RFF feature,recognition results based on the Gaussian mixture model(GMM)classifier further validate the effectiveness of the proposed algorithm.
