北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2021, Vol. 44 ›› Issue (3): 100-105.doi: 10.13190/j.jbupt.2020-197

• 研究报告 • 上一篇    下一篇

基于稳态循环谱特征的通信辐射源识别方法

周楷1, 黄赛1,2, 曾昱祺3, 高晖1, 卢华2   

  1. 1. 北京邮电大学 信息与通信工程学院, 北京 100876;
    2. 广东省新一代通信与网络创新研究院, 广州 510700;
    3. 国家无线电监测中心, 北京 100037
  • 收稿日期:2020-10-07 出版日期:2021-06-28 发布日期:2021-06-23
  • 通讯作者: 卢华(1976-),男,高级工程师,E-mail:luhua@gdcni.cn. E-mail:luhua@gdcni.cn
  • 作者简介:周楷(1995-),男,硕士生.
  • 基金资助:
    国家重点研发计划项目(2020YFB1807602);国家自然科学基金项目(61801052)

Communication Emitter Identification Method Based on Steady-State Cyclic Spectrum Characteristics

ZHOU Kai1, HUANG Sai1,2, ZENG Yu-qi3, GAO Hui1, LU Hua2   

  1. 1. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China;
    2. Guangdong Communications & Networks Institute, Guangzhou 510700, China;
    3. The State Radio Monitoring Center, Beijing 100037, China
  • Received:2020-10-07 Online:2021-06-28 Published:2021-06-23

摘要: 为实现低信噪比环境下多种通信辐射源的高精度识别,提出了一种基于稳态循环谱特征的通信辐射源识别方法,利用循环谱频域截面谱对高斯噪声的强鲁棒性,提取不同辐射源成形滤波器间的本征差异进行识别.首先对接收到的稳态信号提取循环谱频域截面谱并利用主成分分析方法降维,之后分别采用皮尔逊相关系数法、概率神经网络、弗雷歇距离法等判决方法进行辐射源类别判决.仿真实验显示,该特征使用概率神经网络判决和皮尔逊相关系数法判决,显著优于传统循环频率域的切片特征,证明有一定应用价值.

关键词: 通信辐射源识别, 稳态循环谱特征, 皮尔逊相关系数法, 概率神经网络, 弗雷歇距离法

Abstract: In order to realize high-precision identification of multiple communication emitters in low signal-to-noise ratio (SNR) environment, a method of communication emitter identification based on steady-state cyclic spectrum characteristics is proposed. By using the strong robustness of cyclic spectrum's cross-sectional spectrum in frequency domain to Gaussian noise, the intrinsic differences between shaping filters of different emitters are extracted for identification. Specifically, the cyclic spectrum's cross-sectional spectra in frequency domain are extracted from the received steady-state signals, and the dimensions are reduced by principal component analysis. Then the emitters' categories are determined by Pearson correlation coefficient method, probabilistic neural network and Fréchet distance method, etc. Simulation shows that the proposed feature is superior to the traditional slice feature in cyclic frequency domain by using probabilistic neural network and Pearson correlation coefficient, which proves that it has certain application value.

Key words: communication emitter identification, steady-state cyclic spectrum feature, Pearson correlation coefficient method, probabilistic neural network, Fréchet distance method

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