基于射频指纹的自适应变分模态分解算法
文璐1张琰祥2王鹏1惠鏸2姚俊良2
Adaptive Variational Mode Decomposition Algorithm Based on Radio Frequency Fingerprint
WEN LuZHANG YanxiangWANG PengHUI HuiYAO Junliang
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作者信息:1.中铁第一勘察设计院集团有限公司， 710043， 西安0
2.西安理工大学自动化与信息工程学院， 710048， 西安
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Affiliation:China Railway First Survey and Design Institute Group Co., Ltd., 710043, Xi’an, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2023.08.004
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中图分类号/CLCN:U285.2
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栏目/Col:学术专论
摘要:
目的：城市轨道交通列车运行控制系统主要依赖于无线通信，外部干扰会对其正常运行产生严重威胁。为及时发现外部干扰源，保障轨道交通无线通信系统安全，需研究RFF(射频指纹)识别技术中采用的自适应变分模态分解算法，以提高对轨道交通环境中的无线发射设备身份识别的准确率。方法：在提取RFF后，应用自适应变分模态分解算法，利用分解后各模态之间的相关系数及各模态能量在信号总能量中的占比这两个指标，联合判断分解过程中是否出现了模态混叠及过分解现象。并在此基础上，以迭代的方式选择合适的分解模态数和惩罚因子，从而提高模态分解的准确性。在识别过程中，将由各模态构成的重构信号作为设备的RFF，并利用LSTM(长短期记忆网络)对无线发射设备进行分类识别。结果及结论：试验结果表明，通过自适应变分模态分解算法所获得的重构信号与原信号相比不但保留了较为完整的指纹特征，且噪声得到了一定程度的抑制，说明该算法具有较强的RFF提取能力。在对实际WiFi（无线保真）设备的识别中，自适应变分模态分解算法的识别准确率显著优于现有同类算法，且受噪声的影响程度较轻，说明该算法可有效提高无线发射设备的识别准确率。
Abstracts:
Objective: The operation control system of urban rail transit trains mainly relies on wireless communication, and external interference poses a serious threat to its normal operation. To promptly detect external sources of interference and ensure the safety of wireless communication systems in rail transit, it is necessary to study the adaptive VMD (variational mode decomposition) algorithm used in RFF (radio frequency fingerprinting) identification technology, aiming to improve the accuracy rate of identifying wireless transmitter devices in rail transit environment.Method: After extracting RFF, the adaptive VMD algorithm is applied. Two indicators, namely the correlation coefficients between different modes and the proportion of each mode energy in the total signal energy, are utilized to jointly determine whether mode aliasing or overdecomposition occurs during the decomposition process. Based on this, an iterative approach is employed to select appropriate decomposition mode number and penalty factor, thereby enhancing the accuracy of mode decomposition. In the identification process, the reconstructed signal composed of the different modes is used as the device RFF, and the LSTM (long shortterm memory) is employed for wireless transmitter device classification and identification.Result & Conclusion: Experimental results demonstrate that the reconstructed signal obtained through the adaptive VMD algorithm not only preserves relatively complete fingerprint characteristics compared to the original signal, but also suppresses noise to a certain extent, exhibiting strong RFF extraction capability. In the identification of actual WiFi (wireless fidelity) devices, the identification accuracy of the adaptive VMD algorithm is significantly superior to existing similar programs with less susceptibility to noise, indicating that the algorithm can effectively improve identification accuracy rate of WiFi emission devices.
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