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改进变分模态分解与多特征的通信辐射源个体识别方法

刘高辉 席宏恩

刘高辉, 席宏恩. 改进变分模态分解与多特征的通信辐射源个体识别方法[J]. 电子与信息学报. doi: 10.11999/JEIT231348
引用本文: 刘高辉, 席宏恩. 改进变分模态分解与多特征的通信辐射源个体识别方法[J]. 电子与信息学报. doi: 10.11999/JEIT231348
LIU Gaohui, XI Hongen. Individual Identification Method for Communication Emitters Based on Improved Variational Modal Decomposition and Multiple Features[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231348
Citation: LIU Gaohui, XI Hongen. Individual Identification Method for Communication Emitters Based on Improved Variational Modal Decomposition and Multiple Features[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231348

改进变分模态分解与多特征的通信辐射源个体识别方法

doi: 10.11999/JEIT231348
基金项目: 国家自然科学基金(61671375)
详细信息
    作者简介:

    刘高辉:男,博士,教授,硕士生导师,主要研究方向为通信信号处理、认知无线电、通信辐射源识别和无源探测等

    席宏恩:男,硕士生,研究方向为通信辐射源个体识别

    通讯作者:

    席宏恩 2210320062@stu.xaut.edu.cn

  • 中图分类号: TN911

Individual Identification Method for Communication Emitters Based on Improved Variational Modal Decomposition and Multiple Features

Funds: The National Natural Science Foundation of China (61671375)
  • 摘要: 针对通信辐射源指纹特征难以提取和单一特征识别率不高的问题,并考虑到通信辐射源细微特征的非线性、非平稳特点,该文提出了一种基于改进变分模态分解和多特征的通信辐射源个体识别方法。首先,为了获得变分模态分解的分解层数和惩罚因子的最优组合,采用鲸鱼优化算法对通信辐射源符号波形信号的变分模态分解方法进行了改进,该方法以序列复杂度为停止准则,使每个符号波形信号能够自适应地分解出包含非线性指纹特征的高频信号分量和数据信息的低频分量;然后,根据相关阈值选取能够最佳表征辐射源非线性特征的高频信号分量层数,分别对其提取模糊熵、排列熵、Higuchi维数以及Katz维数并组成多域联合特征向量;最后,通过卷积神经网络实现通信辐射源个体识别分类,利用ORACLE公开数据集进行实验。实验结果表明:该方法有较高的识别精度且具有良好的抗噪声性能。
  • 图  1  通信辐射源发射机系统模型

    图  2  WOA-VMD流程框图

    图  3  5个辐射源信号熵特征分布图

    图  4  5个辐射源信号分形维数特征分布图

    图  5  通信辐射源个体识别算法流程图

    图  6  5个OFDM辐射源信号对应的标准化特征值分布图

    图  7  测试集混淆矩阵

    图  8  不同信噪比下算法识别率对比曲线

    图  9  与现有方法的识别率对比曲线

    表  1  不同层数在不同信噪比下的识别率(%)

    层数 信噪比SNR(dB)
    –4 dB –2 dB 0 dB 2 dB 4 dB
    3 66.5 69.5 72.1 73.4 75.3
    4 64.2 71 73.2 80.3 81
    5 67.1 73.8 76 83.1 89
    6 69.5 77.9 82 89.8 96.3
    7 68.8 78 83.3 89.1 92.6
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-12-05
  • 修回日期:  2024-09-05
  • 网络出版日期:  2024-09-11

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