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基于持续学习和联合特征提取的特定辐射源识别

张立民 谭凯文 闫文君 张婷婷 汤淼

张立民, 谭凯文, 闫文君, 张婷婷, 汤淼. 基于持续学习和联合特征提取的特定辐射源识别[J]. 电子与信息学报, 2023, 45(1): 308-316. doi: 10.11999/JEIT211176
引用本文: 张立民, 谭凯文, 闫文君, 张婷婷, 汤淼. 基于持续学习和联合特征提取的特定辐射源识别[J]. 电子与信息学报, 2023, 45(1): 308-316. doi: 10.11999/JEIT211176
ZHANG Limin, TAN Kaiwen, YAN Wenjun, ZHANG Tingting, TANG Miao. Specific Emitter Identification Based on Continuous Learning and Joint Feature Extraction[J]. Journal of Electronics & Information Technology, 2023, 45(1): 308-316. doi: 10.11999/JEIT211176
Citation: ZHANG Limin, TAN Kaiwen, YAN Wenjun, ZHANG Tingting, TANG Miao. Specific Emitter Identification Based on Continuous Learning and Joint Feature Extraction[J]. Journal of Electronics & Information Technology, 2023, 45(1): 308-316. doi: 10.11999/JEIT211176

基于持续学习和联合特征提取的特定辐射源识别

doi: 10.11999/JEIT211176
基金项目: 国家自然科学基金(91538201),泰山学者工程专项经费基金(ts201511020)
详细信息
    作者简介:

    张立民:男,教授,博士生导师,研究方向为卫星信号处理及应用

    谭凯文:男,硕士生,研究方向为特定辐射源识别

    闫文君:男,博士,副教授,研究方向为空时分组码检测

    张婷婷:女,硕士生,研究方向为飞行姿态识别

    汤淼:男,硕士生,研究方向为计算机兵力对抗生成

    通讯作者:

    谭凯文 1326097124@qq.com

  • 中图分类号: TN911.7

Specific Emitter Identification Based on Continuous Learning and Joint Feature Extraction

Funds: The National Natural Science Foundation of China (91538201), Taishan Scholars Project Special Fund (ts201511020)
  • 摘要: 针对特定辐射源识别(SEI)识别准确率较低和单次样本学习花销较大的问题,该文提出一种基于增量式学习的SEI方法,设计多个连续增量深度极限学习机(CIDELM)。从截获信号中分别提取变分模态分解(VMD)后的Hilbert谱投影和高阶谱,降维后作为射频指纹(RFF)用于分类;在极限学习机(ELM)中采用稀疏自编码结构对多个隐含层进行无监督训练,并利用参数搜索策略确定最佳隐含层数和隐节点个数,实现对多批次标记样本的连续在线匹配。实验结果表明,该方法对不同调制方式、载波频率和收发距离均能表现出良好兼容性,能够实现对于多个辐射源个体的有效识别。
  • 图  1  基于联合特征提取的SEI框架

    图  2  VMD处理后的3D-Hilbert谱及时频域投影

    图  3  DELM结构

    图  4  高阶谱分析-CIDELM识别性能

    图  5  VMD谱灰度向量-CIDELM识别性能

    图  6  识别性能随收发距离变化曲线

    图  7  不同方法识别效果对比

    表  1  算法时间复杂度分析

    辐射源数量平均迭代时间(s)平均识别时间(s)
    K = 351.175.06
    K = 456.485.57
    K = 565.345.56
    K = 670.556.41
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-10-28
  • 修回日期:  2022-05-16
  • 网络出版日期:  2022-05-24
  • 刊出日期:  2023-01-17

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