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弹箭与制导学报 >

2021 , Vol. 41 >Issue 3: 25 - 30

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2021.03.006

基于小样本条件的雷达辐射源个体识别算法

  • 丁辰伟 1 ,
  • 孙闽红 1 ,
  • 官友廉 2
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  • 1 杭州电子科技大学通信工程学院,杭州 310000
  • 2 75798部队,北京 100089

丁辰伟(1995—),男,浙江嘉兴人,硕士研究生,研究方向:信号处理与辐射源个体识别。

收稿日期: 2019-12-21

  网络出版日期: 2025-02-20

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Algorithm of Specific Radar Emitter Identification Based on Few-shot Learning

  • DING Chenwei 1 ,
  • SUN Minhong 1 ,
  • GUAN Youlian 2
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  • 1 School of Communication Engineering, Hangzhou Dianzi University,Hangzhou 310000, China
  • 2 No.75798 Unit, Beijing 100089, China

Received date: 2019-12-21

  Online published: 2025-02-20

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摘要

针对小样本条件下雷达辐射源个体识别率不高问题,提出了一种基于图像增强技术以及生成对抗网络(GAN)的雷达辐射源个体识别性能的算法,并对真实采集信号开展了算法性能验证与比较实验。实验结果表明:加入图像特征增强方法后,识别性能有明显提高,直方图均衡化特征增强法性能最优,识别率较改进前平均提高了9 dB。加入增强条件对抗生成网络(SCGAN)后,算法在小样本条件下的识别性能平均提升15 dB,所需样本数量减少了75%。

关键词： 特定辐射源识别; 卷积神经网络; 稀疏自编码器; 生成对抗网络; 特征增强

本文引用格式

丁辰伟 , 孙闽红 , 官友廉 . 基于小样本条件的雷达辐射源个体识别算法[J]. 弹箭与制导学报, 2021 , 41(3) : 25 -30 . DOI: 10.15892/j.cnki.djzdxb.2021.03.006

Abstract

To solve the problem of specific radar emitter identification (SREI), an algorithm of SREI based on feature enhancement and generative adversarial networks is proposed, and the experiments based on real acquisition signals are carried out. The experimental results show that the identification performance of the proposed algorithm is obviously improved after the feature enhancement, and the histogram equalization feature enhancement method has the best performance. The recognition performance has averagely 9 dB better than that of the algorithm without feature enhancement. After adding SCGAN, the recognition performance of the algorithm is improved by 15 dB on average, and the number of samples needed is reduced by 75%.

Key words： special emitter identification; convolutional neural network; sparse auto-encoder; generative adversarial network; feature enhancement

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