Joint Variational Modal Decomposition for Specific Emitter Identification With Multiple Sensors

IF 6.3 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS IEEE Transactions on Information Forensics and Security Pub Date : 2024-10-17 DOI:10.1109/TIFS.2024.3482861
Xiaofang Chen;Xue Fu;Wenbo Xu;Yue Wang;Guan Gui
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Abstract

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.
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利用多个传感器识别特定发射器的联合变式模态分解
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,传统的只使用一个传感器的特定发射器识别(SEI)已扩展到使用多个传感器的情况。然而,不同传感器的固有失真会对发射器的射频指纹(RFF)产生独立影响,这不可避免地导致不同传感器提取的特征不通用。此外,变异模态分解(VMD)是 SEI 中一种有效的预处理方法,但在噪声场景中还没有得到很好的研究。为了消除环境噪声,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 利用了多个传感器的中心频率和本征模态函数 (IMF) 的一致性,而 C-JVMD 则进一步估计并滤除每个传感器上可能会扭曲发射器 RFF 的相位噪声。我们在两个实际数据集上模拟了所提出的 JVMD 算法及其在 SEI 中的相应应用。与传统的 VMD 相比,所提出的算法提高了设备分类的准确性和对噪声的鲁棒性。
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来源期刊
IEEE Transactions on Information Forensics and Security
IEEE Transactions on Information Forensics and Security 工程技术-工程:电子与电气
CiteScore
14.40
自引率
7.40%
发文量
234
审稿时长
6.5 months
期刊介绍: The IEEE Transactions on Information Forensics and Security covers the sciences, technologies, and applications relating to information forensics, information security, biometrics, surveillance and systems applications that incorporate these features
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