小样本条件下基于数据增强和WACGAN的雷达目标识别算法

doi:10.3969/j.issn.0372-2112.2020.06.012

电子学报 ›› 2020, Vol. 48 ›› Issue (6): 1124-1131.DOI: 10.3969/j.issn.0372-2112.2020.06.012

小样本条件下基于数据增强和WACGAN的雷达目标识别算法

朱克凡, 王杰贵, 刘有军

国防科技大学电子对抗学院, 安徽合肥 230037

收稿日期:2019-06-18 修回日期:2019-11-07 出版日期:2020-06-25
- 作者简介:
- 朱克凡 男,1994年出生,山东青岛人.现为国防科技大学硕士研究生,主要研究方向为雷达目标识别.
  王杰贵 男,1969年生,现为国防科技大学电子对抗学院副教授、硕士生导师,研究方向为雷达信号处理.
  刘有军 男,1975年生,现为国防科技大学电子对抗学院副教授,研究方向为电子对抗信息处理和装备计量技术研究.
- 基金资助:
- 国防预研基金 (No.9140C100404120C1003）

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

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

Electronic Countermeasure Institute of National University of Defense Technology, Hefei, Anhui 230037, China

Received:2019-06-18 Revised:2019-11-07 Online:2020-06-25 Published:2020-06-25

摘要/Abstract

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.

关键词: 雷达目标识别, 数据增强, 生成对抗网络

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.

Key words: radar target recognition, data augmentation, generative adversarial network

中图分类号:

TN959.1+7

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.

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.

参考文献

[1] Feixiang Zhao,Liu Y,Huo K,et al.Radar HRRP target recognition based on stacked autoencoder and extreme learning machine[J].Sensors,2018,18(1):173.
[2] Feng B,Chen B,Liu H.Radar HRRP target recognition with deep networks[J].Pattern Recognition,2017,61:379-393.
[3] 陈志仁,顾红,苏卫民,王钊.改进的支持向量机低分辨雷达目标分类算法[J].系统工程与电子技术,2017,39(11):2456-2462. CHEN Z R,GU H,SU W M,et al.Improved support vector machine target classification algorithm for low-resolution radar[J].Systems Engineering and Electronics,2017,39(11):2456-2462.(in Chinese)
[4] HU G,WANG K J,PENG Yuan,et al.Deep learning methods for underwater target feature extraction and recognition[J].Computational Intelligence and Neuroscience,2018,2018(3):1-10.
[5] Ding J,Chen B,Liu H,et al.Convolutional neural network with data augmentation for SAR target recognition[J].IEEE Geoscience and Remote Sensing Letters,2016,13(3):364-368.
[6] SUN X J,LI J R,GU L,et al.Identifying the characteristics of the hypusination sites using SMOTE and SVM algorithm with feature selection[J].Current Proteomics,2018,15(2):111-118.
[7] FIORE U,SANTIS A D,PERLA F,et al.Using generative adversarial networks for improving classification effectiveness in credit card fraud detection.[J].Information Sciences,2017,479:448-455.
[8] HU C,XIONG D H,ZHANG Q Y.et al.Parallel connected generative adversarial network with quadratic operation for SAR image generation and application for classification[J].Sensors 2019,19(4):871.
[9] IlYASS H,JAYANT S G,VINCENT F.Anomaly detection with Wasserstein GAN[DB/OL].https://arxiv.org/pdf/1812.02463.pdf,2018-12-02.
[10] VIVEK K S,SANTIAGO R,HATEM A R,et al.Conditional Generative Adversarial and Convolutional Networks for X-ray Breast Mass Segmentation and Shape Classification[DB/OL].https://arxiv.org/pdf/1805.10207.pdf,2018-05-10.
[11] VIVEK K S,HATEM A.R,SANTIAGO R,et al.Breast Tumor Segmentation and Shape Classification in Mammograms Using Generative Adversarial and Convolutional Neural Network[DB/OL].https://arxiv.org/pdf/1809.01687.pdf,2018-09-01.
[12] Shorten C,Khoshgoftaar T M.A Survey on Image Data Augmentation for Deep Learning.[DB/OL].https://link.springer.com/article/10.1186/s40537-019-0197-0,2019-06-01.
[13] GOODFELLOW I J,POUGET Abadie J,MIRZA M,XU B,et al.Generative adversarial nets[A].Proceedings of the 27th International Conference on Neural Information Processing Systems[C].New York,Curran Associates,2014.2672-2680.
[14] ODENA A,OLAH C,SHLENS J.Conditional Image Synthesis with Auxiliary Classifier GANs[DB/OL].https://arxiv.org/pdf/1610.09585.pdf,2016-10-09.
[15] LIN T Y,GOYAL P,GIRSHICK R,et al.Focal loss for dense object detection[A].IEEE International Conference on Computer Vision[C].Washington:IEEE Computer Society,2017.2999-3007.
[16] ALEC R,LUKE M,SOUMITH C.Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks[DB/OL].https://arxiv.org/pdf/1511.06434.pdf,2015-11-06.

小样本条件下基于数据增强和WACGAN的雷达目标识别算法

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

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	吴靖, 叶晓晶, 黄峰, 陈丽琼, 王志锋, 刘文犀. 基于深度学习的单帧图像超分辨率重建综述[J]. 电子学报, 2022, 50(9): 2265-2294.
[2]	张志文, 刘天歌, 聂鹏举. 基于实景数据增强和双路径融合网络的实时街景语义分割算法[J]. 电子学报, 2022, 50(7): 1609-1620.
[3]	裴炤, 邱文涛, 王淼, 马苗, 张艳宁. 基于Transformer动态场景信息生成对抗网络的行人轨迹预测方法[J]. 电子学报, 2022, 50(7): 1537-1547.
[4]	刘文杰, 赵胶胶, 张颖, 葛业波. 一种量子条件生成对抗网络算法[J]. 电子学报, 2022, 50(7): 1586-1593.
[5]	孙晨峰, 吕卫民, 戴洪德, 张浩晨. 一种基于TimeGAN和OCSVM的多元退化设备小子样数据增广方法[J]. 电子学报, 2022, 50(11): 2678-2687.
[6]	李宝奇, 黄海宁, 刘纪元, 李宇. 基于改进CycleGAN的光学图像迁移生成水下小目标合成孔径声纳图像算法研究[J]. 电子学报, 2021, 49(9): 1746-1753.
[7]	席亮, 刘涵, 樊好义, 张凤斌. 基于深度对抗学习潜在表示分布的异常检测模型[J]. 电子学报, 2021, 49(7): 1257-1265.
[8]	陈星宇, 叶锋, 黄添强, 翁彬, 陈家祯, 林文忠. 融合小型深度生成模型的显著性检测[J]. 电子学报, 2021, 49(4): 768-774.
[9]	吴文, 万毅. 基于低尺度细节恢复的单幅图像阴影去除方法[J]. 电子学报, 2020, 48(7): 1293-1302.
[10]	王格格, 郭涛, 余游, 苏菡. 基于生成对抗网络的无监督域适应分类模型[J]. 电子学报, 2020, 48(6): 1190-1197.
[11]	江泽涛, 覃露露. 一种基于U-Net生成对抗网络的低照度图像增强方法[J]. 电子学报, 2020, 48(2): 258-264.
[12]	方晨, 郭渊博, 王娜, 甄帅辉, 唐国栋. 基于生成对抗网络的差分隐私数据发布方法[J]. 电子学报, 2020, 48(10): 1983-1992.
[13]	甘俊英, 翟懿奎, 黄聿, 曾军英, 姜开永. 基于双激活层深度卷积特征的人脸美丽预测研究[J]. 电子学报, 2019, 47(3): 636-642.
[14]	范梅梅;廖东平;丁小峰;黎湘. 基于WLS-TIR的多目标识别认知雷达波形自适应方法[J]. 电子学报, 2012, 40(1): 73-77.
[15]	付耀文;贾宇平;庄钊文. 基于一维散射中心匹配的雷达目标识别[J]. 电子学报, 2006, 34(3): 404-408.