Radar Target Recognition Algorithm Based on Data Augmentation and WACGAN with a Limited Training Data

ZHUKe-fan; WANG Jie-gui; LIU You-jun

doi:10.3969/j.issn.0372-2112.2020.06.012

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Radar Target Recognition Algorithm Based on Data Augmentation and WACGAN with a Limited Training Data

更新时间：2025-07-16

- Radar Target Recognition Algorithm Based on Data Augmentation and WACGAN with a Limited Training Data
- Acta Electronica Sinica Vol. 48, Issue 6, Pages: 1124-1131(2020)
- 作者机构：
  
  国防科技大学电子对抗学院,安徽,合肥,230037
- 作者简介：
- 基金信息：
  
  National Defense Pre-research Foundation of China (No.9140C100404120C1003)
- DOI：10.3969/j.issn.0372-2112.2020.06.012
  CLC： TN959.1+7
- Published Online：25 June 2020，
  
  Published：2020
- 稿件说明：
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ZHUKe-fan, WANG Jie-gui, LIU You-jun. Radar Target Recognition Algorithm Based on Data Augmentation and WACGAN with a Limited Training Data[J]. Acta Electronica Sinica, 2020, 48(6): 1124-1131.
DOI：

ZHUKe-fan, WANG Jie-gui, LIU You-jun. Radar Target Recognition Algorithm Based on Data Augmentation and WACGAN with a Limited Training Data[J]. Acta Electronica Sinica, 2020, 48(6): 1124-1131. DOI： 10.3969/j.issn.0372-2112.2020.06.012.

摘要

目前小样本条件下高分辨距离像雷达目标识别算法存在识别率较低、识别率稳定度较差等问题，对此，本文提出了基于数据增强和加权辅助分类生成对抗网络（Weighted Auxiliary Classifier Generative Adversarial Networks，WACGAN）的雷达目标识别算法.该算法首先根据雷达目标散射特性，通过时间镜像数据增强方法扩充数据集，然后将扩充数据集输入WACGAN，通过自动选择高质量的生成样本，使判别器在标签样本监督学习的基础上得到进一步优化，最后直接利用判别器实现对雷达目标的有效识别.仿真实验结果表明，本文算法在不增加识别时间的基础上，有效提高了小样本条件下对雷达目标的识别率和识别稳定度.

Abstract

At present

the existing high-resolution range profile radar target recognition algorithm with limited training data still has several drawbacks (e.g.

low recognition accuracy and poor recognition stability). To this end

an efficient radar target recognition algorithm is developed in this paper

which is based on data augmentation and Weighted Auxiliary Classifier Generative Adversarial Networks (WACGAN). Specifically

we expand data set by using the data augmentation method based on time mirroring

and the radar target scattering characteristics is considered. After that

the WACGAN with expanded data set is used to automatically select high-quality generated samples and further optimize the discriminator

which has been improved through the supervised learning. Then

the optimized discriminator is used to recognize radar target. Finally

several numerical experiments have been carried out to demonstrate that

under the condition of limited training data

the proposed algorithm possesses higher recognition accuracy and better recognition stability without increasing recognition time.

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