Multi-Function Radar Working Mode Recognition with Few Samples Based on Knowledge Embedded Prototype Network

LI Qiang; ZHANG Wei; JIN Qiu-yuan; YAO Xin

doi:10.12263/DZXB.20210932

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Multi-Function Radar Working Mode Recognition with Few Samples Based on Knowledge Embedded Prototype Network

Intelligent game of electromagnetic spectrum | 更新时间：2025-12-08

- Multi-Function Radar Working Mode Recognition with Few Samples Based on Knowledge Embedded Prototype Network
- ACTA ELECTRONICA SINICA Vol. 50, Issue 6, Pages: 1344-1350(2022)
- 作者机构：
  
  1.电子科技大学信息与通信工程学院，四川成都 611731
  2.鹏城实验室，广东深圳 518055
  3.电子信息控制重点实验室，四川成都 610036
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20210932
  CLC： TP911.7;TP391
- Received：16 July 2021，
  
  Revised：2021-12-29，
  
  Published：25 June 2022
- 稿件说明：
移动端阅览
利强,张伟,金秋园等.基于知识原型网络的小样本多功能雷达工作模式识别[J].电子学报,2022,50(06):1344-1350.

LI Qiang,ZHANG Wei,JIN Qiu-yuan,et al.Multi-Function Radar Working Mode Recognition with Few Samples Based on Knowledge Embedded Prototype Network[J].ACTA ELECTRONICA SINICA,2022,50(06):1344-1350.
利强,张伟,金秋园等.基于知识原型网络的小样本多功能雷达工作模式识别[J].电子学报,2022,50(06):1344-1350. DOI： 10.12263/DZXB.20210932.

LI Qiang,ZHANG Wei,JIN Qiu-yuan,et al.Multi-Function Radar Working Mode Recognition with Few Samples Based on Knowledge Embedded Prototype Network[J].ACTA ELECTRONICA SINICA,2022,50(06):1344-1350. DOI： 10.12263/DZXB.20210932.

摘要

在认知电子战中，对多功能雷达工作模式的识别是至关重要的一个环节.在实际中，由于多功能雷达工作模式的多样性、隐藏性，能侦收到的不同工作模式脉冲样本数可能较少.因此，如何在少量样本条件下，准确识别多功能雷达的工作模式，对雷达对抗具有重要意义.针对此问题，本文提出了一种将模式先验知识与原型网络相融合的识别方法.该方法的核心是将雷达工作模式先验知识进行编码映射，并融入原型网络训练，实现知识在网络模型中的内嵌，以在少量训练样本条件下获得更好的识别性能.仿真结果表明，融入了先验知识的原型网络与不使用先验知识的原型网络、SVM分类器相比，识别准确率分别提升了2.9%和10.5%.

Abstract

Multifunctional radar working mode recognition is important for cognitive electronic warfare. In practical applications

due to the diversity and concealment of multifunctional radar operating modes

the intercepted pulses for different operating modes is limited. Therefore

using only limited intercepted pulse records to accurately recognize the modes of the radar is a challenging but important task for radar countermeasures. To address the above problem

this paper proposes a novel recognition method by integrating the prior knowledge with the prototype network. The core of this method is to encode and embed the prior knowledge into prototype network training to obtain better recognition performance with few training samples. The simulation results show that compared with prototype networks and SVM that do not use prior knowledge

the recognition accuracy of the prototype network with prior knowledge is increased by 2.9% and 10.5%

respectively.

关键词

Keywords

references

LI Y J , ZHU M T , MA Y H , et al . Work modes recognition and boundary identification of MFR pulse sequences with a hierarchical seq2seq LSTM [J]. IET Radar , Sonar & Navigation, 2020 , 14 ( 9 ): 1343 - 1353 .

王玉冰 , 程嗣怡 , 周一鹏 , 等 . 基于DS证据理论的机载火控雷达空空工作模式判定 [J]. 现代雷达 , 2017 , 39 ( 5 ): 79 - 84 .

WANG Y B , CHENG S Y , ZHOU Y P , et al . Air-to-air operation modes recognition of airborne fire control radar based on DS evidence theory [J]. Modern Radar , 2017 , 39 ( 5 ): 79 - 84 . (in Chinese)

董晓璇 , 程嗣怡 , 周一鹏 , 等 . 基于时域模糊决策融合的雷达工作模式识别方法 [J]. 空军工程大学学报(自然科学版) , 2018 , 19 ( 6 ): 59 - 65 .

DONG X X , CHENG S Y , ZHOU Y P , et al . A recognition method of radar operation modes based on time-domain fuzzy decision fusion [J]. Journal of Air Force Engineering University(Natural Science Edition) , 2018 , 19 ( 6 ): 59 - 65 . (in Chinese)

陈维高 , 贾鑫 , 朱卫纲 , 等 . 基于HMM的雷达状态转移估计方法 [J]. 北京航空航天大学学报 , 2017 , 43 ( 10 ): 2171 - 2180 .

CHEN W G , JIA X , ZHU W G , et al . Radar state transfer estimation method based on HMM [J]. Journal of Beijing University of Aeronautics and Astronautics , 2017 , 43 ( 10 ): 2171 - 2180 . (in Chinese)

刘海军 . 雷达辐射源识别关键技术研究 [D]. 长沙 : 国防科学技术大学 , 2010 .

LIU H J . Researches on Identification Key Technology for Radar Emitter [D]. Changsha : National University of Defense Technology , 2010 . (in Chinese)

贠洁 , 孙闽红 , 官友廉 . 基于雷达字建模的多功能雷达工作模式识别 [J]. 杭州电子科技大学学报(自然科学版) , 2020 , 40 ( 6 ): 19 - 25 .

YUN J , SUN M H , GUAN Y L . Recognizing multi-function radar working mode based on radar word modeling [J]. Journal of Hangzhou Dianzi University(Natural Sciences) , 2020 , 40 ( 6 ): 19 - 25 . (in Chinese)

秦鑫 , 黄洁 , 查雄 , 等 . 基于扩张残差网络的雷达辐射源信号识别 [J]. 电子学报 , 2020 , 48 ( 3 ): 456 - 462 .

QIN X , HUANG J , ZHA X , et al . Radar emitter signal recognition based on dilated residual network [J]. Acta Electronica Sinica , 2020 , 48 ( 3 ): 456 - 462 . (in Chinese)

马爽 . 多功能雷达电子情报信号处理关键技术研究 [D]. 长沙 : 国防科学技术大学 , 2013 .

MA S . Research on ELINT Signal Processing Key Technologies for Multifunction Radar [D]. Changsha : National University of Defense Technology , 2013 . (in Chinese)

刘俊江 . 机载火控雷达工作模式识别 [J]. 电子测量技术 , 2016 , 39 ( 2 ): 131 - 133, 146 .

LIU J J . Airborne fire control radar work modes recognition [J]. Electronic Measurement Technology , 2016 , 39 ( 2 ): 131 - 133, 146 . (in Chinese)

OU J , CHEN Y G , ZHAO F , et al . Method for operating mode identification of multi-function radars based on predictive state representations [J]. IET Radar , Sonar & Navigation, 2017 , 11 ( 3 ): 426 - 433 .

朱克凡 , 王杰贵 , 刘有军 . 小样本条件下基于数据增强和WACGAN的雷达目标识别算法 [J]. 电子学报 , 2020 , 48 ( 6 ): 1124 - 1131 .

ZHU K F , WANG J G , LIU Y J . Radar target recognition algorithm based on data augmentation and WACGAN with a limited training data [J]. Acta Electronica Sinica , 2020 , 48 ( 6 ): 1124 - 1131 . (in Chinese)

金秋园 . 小样本雷达工作模式的识别研究 [D]. 成都 : 电子科技大学 , 2021 .

JIN Q Y . Research of Radar Working Mode Recognition with few Samples [D]. Chengdu : University of Electronic Science and Technology of China , 2021 . (in Chinese)

赵凯琳 , 靳小龙 , 王元卓 . 小样本学习研究综述 [J]. 软件学报 , 2021 , 32 ( 2 ): 349 - 369 .

ZHAO K L , JIN X L , WANG Y Z . Survey on few-shot learning [J]. Journal of Software , 2021 , 32 ( 2 ): 349 - 369 . (in Chinese)

刘颖 , 雷研博 , 范九伦 , 等 . 基于小样本学习的图像分类技术综述 [J]. 自动化学报 , 2021 , 47 ( 2 ): 297 - 315 .

LIU Y , LEI Y B , FAN J L , et al . Survey on image classification technology based on small sample learning [J]. Acta Automatica Sinica , 2021 , 47 ( 2 ): 297 - 315 . (in Chinese)

樊笛 , 巨志勇 . 基于原型网络的小样本图像识别方法 [J]. 计算机与现代化 , 2020 , ( 3 ): 103 - 107 .

FAN D , JU Z Y . Method of small sample image recognition based on prototype network [J]. Computer and Modernization , 2020 , ( 3 ): 103 - 107 . (in Chinese)

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