基于增强积分双谱的轨道交通辐射源识别方法
刘海川1张可欣2惠鏸2文璐1,3
Rail Transit Radiation Source Identification Method Based on Enhanced Diagonal Integral Bispectrum
LIU Haichuan1ZHANG Kexin2HUI Hui2WEN Lu1,3
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作者信息:1.轨道交通工程信息化国家重点试验室， 710043， 西安
2.西安理工大学自动化与信息工程学院， 710048， 西安
3.中铁第一勘察设计院集团有限公司， 710043， 西安
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Affiliation:1.State Key Laboratory of Rail Transit Engineering Informatization, 710043, Xi’an, China
2.School of Automation and Information Engineering, Xi’an University of Technology, 710048, Xi’an, China
3.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.2024.01.004
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中图分类号/CLCN:U231.7
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栏目/Col:学术专论
摘要:
［目的］城市轨道交通无线通信系统中存在大量外部干扰信号，对行车安全构成重大隐患。针对辐射源射频特征易受噪声与干扰影响，导致识别准确率低的问题，须提出一种基于增强对角积分双谱的通信辐射源个体识别方法，为轨道交通无线通信系统安全保障提供有效新途径。［方法］分析了对角相关局部积分双谱(DCLIB)的数据处理过程及原理，阐述了双谱变换的计算、增强对角积分双谱的计算、自适应双谱积分区间的划分，以及基于残差网络的辐射源识别方法。基于实际Wi-Fi(无线保真)设备进行仿真试验，对DCLIB方法和其他辐射源识别方法的识别效果进行分析对比。［结果及结论］DCLIB方法先估计通信辐射源信号的双谱，并利用次对角线各平行线的自相关特性形成新的谱信息以增强信号的细微特征；然后依据谱信号强度自适应选取合理的谱信号积分区间，在降低噪声影响的同时降低算法的计算复杂度，从而获得增强的对角积分双谱；进而将所提DCLIB信号作为辐射源的射频指纹特征，采用深度残差网络实现辐射源个体识别。基于实际Wi-Fi设备的仿真识别试验结果表明，DCLIB方法的识别准确率最优，并具有良好的抗噪声性能。
Abstracts:
［Objective］ Numerous external interference signals exist in urban rail transit wireless communication system, posing a significant threat to operational safety. Targeting the issue of low identification accuracy due to the radiation source RF (radio frequency) characteristics susceptible to noise and interference, it is necessary to propose an individual identification method for communication radiation sources based on enhanced diagonal integral bispectrum. This method provides an effective new approach to ensuring the security of rail transit wireless communication systems. ［Method］ The data processing procedure and principles of DCLIB (diagonal-correlation local-integral bispectrum) are analyzed. The calculations for bispectrum transformation, enhanced diagonal integral bispectrum calculation, division of adaptive bispectrum integration interval, and radiation source identification method based on residual networks are explained. Simulation experiments are conducted using actual Wi-Fi (wireless fidelity) devices to analyze and compare the identification performance of the DCLIB method with that of other radiation source identification methods. ［Result & Conclusion］ The DCLIB method first estimates the bispectrum of communication radiation source signals and utilizes the autocorrelation characteristics of each parallel line on the sub-diagonals to form new spectral information for the enhancement of the signal subtle features. Subsequently, the method adaptively selects a reasonable spectral signal integration interval based on the spectral signal strength, reducing both noise impact and algorithm computational complexity. Thus, an enhanced diagonal integral bispectrum is obtained. The proposed DCLIB signal is then used as the RF fingerprint feature of the radiation source, and individual source identification is achieved using a deep residual network. Simulation identification experiments based on actual Wi-Fi devices demonstrate that the DCLIB method achieves the highest identification accuracy and exhibits excellent noise-resistance performance.
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