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Specific Emitter Identification Based on Variational Mode Decomposition and Spectral Features in Single Hop and Relaying Scenarios

机译:单跳和中继场景中基于变分模式分解和频谱特征的特定辐射源识别

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Specific emitter identification is the process of identifying or discriminating different emitters based on the radio frequency fingerprints extracted from the received signal. Due to inherent non-linearities of the power amplifiers of emitters, these fingerprints provide distinguish features for emitter identification. In this paper, we develop an emitter identification based on variational mode decomposition and spectral features (VMD-SF). As VMD decomposes the received signal simultaneously into various temporal and spectral modes, we choose to explore different spectral features, including spectral flatness, spectral brightness, and spectral roll-off for improving the identification accuracy contrary to existing temporal features-based methods. For demonstrating the robustness of VMD in decomposing the received signal into emitter-specific modes, we also develop a VMD-entropy and moments (EMn2n) method based on existing temporal features extracted from the Hilbert Huang transform of the emitter-specific temporal modes. Our proposed method has three major steps: received signal decomposition using VMD, feature extraction, and emitter identification. We evaluate the performance of the proposed methods using the probability of correct classification (Fnccn) both in single hop and in relaying scenario by varying the number of emitters. To demonstrate the superior performance of our proposed methods, we compared our methods with the existing empirical mode decomposition-(entropy-, first-, and second-order moments) (EMD-EMn2n) method both in terms of Fnccnand computational complexity. Results depict that the proposed VMD-SF emitter identification method outperforms the proposed VMD-EMn2nmethod and the existing EMD-EMn2nmethod both in single hop and relaying scenarios for a varying number of emitters. In addition, the proposed VMD-SF method has lowest computational cost as compared with the aforementioned methods.
机译:特定发射器标识是基于从接收信号中提取的射频指纹来标识或区分不同发射器的过程。由于发射器功率放大器的固有非线性,这些指纹为发射器识别提供了鲜明的特征。在本文中,我们基于变分模式分解和光谱特征(VMD-SF)开发了发射器识别。当VMD同时将接收到的信号分解为各种时间和频谱模式时,我们选择探索不同的频谱特征,包括频谱平坦度,频谱亮度和频谱滚降,以提高与现有基于时间特征的方法相反的识别精度。为了证明VMD在将接收到的信号分解为特定于发射器的模式中的鲁棒性,我们还开发了VMD熵和矩(EMn 2 n)方法,该方法基于从特定于发射体的时间模式的希尔伯特·黄(Hilbert Huang)变换中提取的现有时间特征。我们提出的方法包括三个主要步骤:使用VMD分解接收信号,特征提取和发射器识别。我们使用正确分类的概率(Fn ccn)在单跳和中继场景中都可以通过更改发射器的数量来实现。为了证明我们提出的方法的优越性能,我们将我们的方法与现有的经验模式分解(熵,一阶和二阶矩)进行了比较(EMD-EMn 2 n)方法都用Fn cc n和计算复杂度。结果表明,提出的VMD-SF发射器识别方法优于提出的VMD-EMn 2 nmethod和现有的EMD-EMn 2n方法在单跳和中继场景中都适用于不同数量的发射器。另外,与前述方法相比,所提出的VMD-SF方法具有最低的计算成本。

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