基于相频融合特征的相控阵雷达个体识别

李保珠; 马鲁; 李龙辉; 洪涛; 姜文

doi:10.11959/j.issn.1000-436x.2024201

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基于相频融合特征的相控阵雷达个体识别

学术论文 | 更新时间：2025-01-14

- 基于相频融合特征的相控阵雷达个体识别
- Phased array radar individual recognition based on phase-frequency fusion feature
- 通信学报 2024年45卷第12期页码：67-82
- 作者机构：
  
  1.西安电子科技大学杭州研究院，浙江杭州 311200
  2.中国电子科技集团公司第三十六研究所，浙江嘉兴 314000
- 作者简介：
  
  [ "李保珠（1990- ），女，河南周口人，博士，西安电子科技大学副研究员，主要研究方向为电磁信号处理、天线与电波传播等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(62071347)
- DOI：10.11959/j.issn.1000-436x.2024201
  中图分类号： TN97
- 收稿日期：2024-07-10，
  
  修回日期：2024-12-10，
  
  纸质出版日期：2024-12-25
- 稿件说明：
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李保珠,马鲁,李龙辉等.基于相频融合特征的相控阵雷达个体识别[J].通信学报,2024,45(12):67-82.

LI Baozhu,MA Lu,LI Longhui,et al.Phased array radar individual recognition based on phase-frequency fusion feature[J].Journal on Communications,2024,45(12):67-82.
李保珠,马鲁,李龙辉等.基于相频融合特征的相控阵雷达个体识别[J].通信学报,2024,45(12):67-82. DOI： 10.11959/j.issn.1000-436x.2024201.

LI Baozhu,MA Lu,LI Longhui,et al.Phased array radar individual recognition based on phase-frequency fusion feature[J].Journal on Communications,2024,45(12):67-82. DOI： 10.11959/j.issn.1000-436x.2024201.

摘要

针对相控阵雷达信号个体特征产生机理复杂、个体识别难的问题，基于相控阵雷达信号的无意调制特征，提出了融合相位特征和频率特征的个体识别方法。首先，基于波位编排及等相位面方法构建相控阵雷达无意调制信号物理模型，揭示无意调制特征的产生机理；然后，利用双谱方法获得信号双谱图并进行围线积分，提取信号无意调制相位特征，同时利用变分模态分解方法对感知信号进行分解获得本征模态分量，进一步计算模态分量集的能量比差，进而提取信号无意调制频率特征；最后，利用局部保持投影方法将所提取的相位及频率特征相融合，采用树形检索方式的K近邻分类方法完成个体识别。仿真实验结果表明，相比于基于单一个体特征的辐射源识别方法，所提方法具有更高的识别率及效率。

Abstract

To solve the problem of phased-array radar individual identification in the complex electromagnetic environment with wide spectrum

heterogeneous waveforms and strong energy

an unintentional modulation feature extraction method based on phase and frequency fusion was proposed for the individual features carried by phased-array radar signals. Considering that the phased array radar signal was difficult to collect

the number of transceiver components was large

and the unintentional modulation features were complex

the phased array radar unintentional modulation signal model was constructed based on the method of wave-position orchestration and isophase surface. Based on the bispectral method

the signal bispectral map was obtained and perimeter integration was performed to extract the unintentionally modulated phase features of the signal. Based on the variational modal decomposition method

the original signal was decomposed to obtain the modal components

and the energy ratio difference of the set of modal components was further computed to extract the unintentionally modulated frequency features of the signal. Finally

the local holding projection method was used to integrate the phase and frequency features

and the K nearest-neighbor classification method was adopted based on the tree retrieval method to realize the individual identification. Representative numerical results are reported

indicating that the proposed method has higher recognition accuracy and efficiency.

关键词

Keywords

references

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