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

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

海军航空大学信息融合研究所烟台 264001

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
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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

Specific Emitter Identification Based on Continuous Learning and Joint Feature Extraction

Institute of Information Fusion, Naval Aviation University, Yantai 264001, China

Funds: The National Natural Science Foundation of China (91538201), Taishan Scholars Project Special Fund (ts201511020)

摘要

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。
- 特定辐射源识别 /
- 增量学习 /
- 变分模态分解 /
- 高阶谱 /
- 深度极限学习机
Abstract: Considering the problem of low recognition accuracy of Specific Emitter Identification (SEI) and high cost of single training, an SEI scheme based on incremental learning is proposed in this paper, multiple Continuous Incremental Deep Extreme Learning Machine(CIDELM) are designed. The Hilbert spectrum projection and higher-order spectrum processed by Variational Mode Decomposition (VMD) are extracted from the original signal, and they are used as the Radio Fingerprint Feature (RFF) for classification after dimensionality reduction. In the Extreme Learning Machine (ELM), the sparse self-encoding structure is introduced to perform unsupervised training on multiple hidden layers, and the parameter search strategy is used to determine the best number of hidden layers and hidden nodes, realizing online multi-batch labeled samples continuous matching. The results show that the algorithm can show good compatibility with different modulation modes, carrier frequencies and transmission distances, and can effectively identify multiple transmitters.
- Specific Emitter Identification (SEI) /
- Incremental learning /
- Variational Mode Decomposition (VMD) /
- High-order spectrum /
- Deep Extreme Learning Machine(ELM)

HTML全文

图 1 基于联合特征提取的SEI框架

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图 2 VMD处理后的3D-Hilbert谱及时频域投影

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图 3 DELM结构

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图 4 高阶谱分析-CIDELM识别性能

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图 5 VMD谱灰度向量-CIDELM识别性能

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图 6 识别性能随收发距离变化曲线

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图 7 不同方法识别效果对比

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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 = 3 51.17 5.06
K = 4 56.48 5.57
K = 5 65.34 5.56
K = 6 70.55 6.41

下载: 导出CSV

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