电子科技 ›› 2019, Vol. 32 ›› Issue (9): 46-50.doi: 10.16180/j.cnki.issn1007-7820.2019.09.010

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载频稳定度在射频指纹识别中的应用研究

王正伟1,郭虹莉2,杨雪洲1   

  1. 1.四川九洲电器集团有限责任公司,四川 绵阳 621000
    2.四川九洲空管科技有限责任公司,四川 绵阳 621000
  • 收稿日期:2018-09-17 出版日期:2019-09-15 发布日期:2019-09-19
  • 作者简介:王正伟(1981-),男,博士,高级工程师。研究方向:目标识别,射频天线技术。|郭虹莉(1983-),女,工程师。研究方向:空中管制技术。|杨雪洲(1985-),男,博士,高级工程师。研究方向:无线通信技术,目标识别技术,大数据与人工智能技术。
  • 基金资助:
    装备发展部共性技术项目(41412010201)

Radio Frequency Fingerprinting Identification Based on Carrier Frequency Stability

WANG Zhengwei1,GUO Hongli2,YANG Xuezhou1   

  1. 1.Sichuan Jiuzhou Electron Group Co.,Ltd,Mianyang 621000,China
    2.Sichuan Jiuzhou ATC Technology Co.,Ltd,Mianyang 621000,China
  • Received:2018-09-17 Online:2019-09-15 Published:2019-09-19
  • Supported by:
    Equipment Development Department Common Technology Project(41412010201)

摘要:

射频指纹识别是利用辐射源射频特征唯一性对其个体身份进行识别的技术,具有广阔的军民用应用前景。针对传统射频指纹特征稳定度不高、难以工程实现的问题,提出了一种基于辐射源频率偏移和相位噪声等多维频域特性的射频指纹识别方法。该方法以ADS-B信号为例,利用统计原理对频率偏移和相位噪声特征进行量化处理,并利用贝叶斯分类方法对信号进行分类以实现辐射源个体身份的识别。通过半实物生成和实采ADS-B信号数据两种方式对提出的算法进行实验验证。实验结果表明,该方法能够对同型号的辐射源进行有效区分,对真实环境下4个目标的识别正确率达到93.5%。

关键词: 射频指纹识别, 频率偏移, 相位噪声, 载频稳定度, 贝叶斯分类, ADS-B

Abstract:

Radio frequency fingerprinting is a technology that uses the uniqueness of the radio frequency characteristics of the radiation source to identify its individual identity, which has broad application prospects for military and civilian applications. Aiming at the problem that the traditional RF fingerprint features were not stable and difficult to implement, a radio frequency fingerprint identification method based on multi-dimensional frequency domain characteristics such as radiation source frequency offset and phase noise was proposed. Taking the ADS-B signal as an example, statistical principles were utilized to quantify frequency offset and phase noise characteristics, and the Bayesian classification method was applied to classify the signals to finally realize the identification of the individual identity. The algorithm was validated by semi-physical generation and real-time ADS-B signal data. The experimental results showed that the method could effectively distinguish the same type of radiation source, and the recognition rate of the four targets in the real environment was 93.5%.

Key words: radio frequency fingerprinting, frequency offset, phase noise, carrier frequency stability, Bayesian classification, ADS-B

中图分类号: 

  • TP309