Specific emitter identification (SEI) is important to guarantee the security of device administration. Recently, to increase the effectiveness of the recognition, traditional SEI employing only one sensor has been extended to the scenario with multiple sensors. However, the inherent distortion at different sensors impacts the radio frequency fingerprints (RFFs) of the emitter independently, which inevitably leads to the non-universalization of the features extracted at different sensors. Besides, variational modal decomposition (VMD), which is an effective preprocessing in SEI, has not been well investigated in noisy scenarios. To combat the environment noise, this paper proposes two joint VMD (JVMD) algorithms, i.e., JVMD for ignoring the distortions at sensors (I-JVMD) and JVMD for considering the distortions at sensors (C-JVMD). Specifically, I-JVMD exploits the consistency of the central frequencies and intrinsic modal functions (IMFs) of multiple sensors, and C-JVMD further estimates and filters out the phase noise at each sensor that may distort the RFFs of the emitter. Simulations of the proposed JVMD algorithms and their corresponding applications in SEI are provided on two real-world datasets. When compared with the traditional VMD, the proposed ones improve the accuracy of device classification and the robustness towards noise.

中文翻译：

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使用多个传感器进行特定发射机识别的联合变分模态分解


特定发射器识别 （SEI） 对于保证设备管理的安全性非常重要。最近，为了提高识别的有效性，传统的仅使用一个传感器的 SEI 已扩展到具有多个传感器的场景。然而，不同传感器的固有失真会独立影响发射器的射频指纹 （RFF），这不可避免地导致不同传感器提取的特征无法通用化。此外，变分模态分解 （VMD） 是 SEI 中的一种有效预处理，但在嘈杂场景中尚未得到很好的研究。为了对抗环境噪声，本文提出了两种联合 VMD （JVMD） 算法，即忽略传感器失真的 JVMD （I-JVMD） 和考虑传感器失真的 JVMD （C-JVMD）。具体来说，I-JVMD 利用多个传感器的中心频率和固有模态函数 （IMF） 的一致性，C-JVMD 进一步估计并过滤掉每个传感器上可能使发射器 RFF 失真的相位噪声。在两个真实数据集上提供了所提出的 JVMD 算法及其在 SEI 中的相应应用的模拟。与传统的 VMD 相比，所提出的 VMD 提高了器件分类的准确性和对噪声的鲁棒性。