基于物理可解释自编码模型的雷达目标方位稳健识别算法

廖磊瑶

doi:10.12263/DZXB.20240024

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基于物理可解释自编码模型的雷达目标方位稳健识别算法

学术论文 | 更新时间：2025-12-11

- 基于物理可解释自编码模型的雷达目标方位稳健识别算法
- High-Range Resolution Radar Target Recognition Based on Deep Interpretable Probabilistic Auto-Encoder Model
- 电子学报 2024年52卷第11期页码：3847-3857
- 作者机构：
  
  1.南京邮电大学通信与信息工程学院，江苏南京 210003
  2.西安电子科技大学雷达信号处理全国重点实验室，陕西西安 710071
- 作者简介：
  
  [ "廖磊瑶女，1996年4月出生，江西抚州人.南京邮电大学通信与信息工程学院讲师.主要研究方向为深度学习、雷达信号处理、雷达目标检测识别、雷达通信感知一体化等.中国电子学会会员编号：E190159131M.E-mail： 20230117@njupt.edu.cn" ]
- 基金信息：
  
  南京邮电大学引进人才科研启动基金（自然科学）(NY223135)
- DOI：10.12263/DZXB.20240024
  中图分类号： TN959.1+7;
- 收稿：2024-01-04，
  
  修回：2024-04-09，
  
  纸质出版：2024-11-25
- 稿件说明：
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廖磊瑶. 基于物理可解释自编码模型的雷达目标方位稳健识别算法[J]. 电子学报, 2024, 52(11): 3847-3857.

LIAO Lei-yao. High-Range Resolution Radar Target Recognition Based on Deep Interpretable Probabilistic Auto-Encoder Model[J]. Acta Electronica Sinica, 2024, 52(11): 3847-3857.
廖磊瑶. 基于物理可解释自编码模型的雷达目标方位稳健识别算法[J]. 电子学报, 2024, 52(11): 3847-3857. DOI：10.12263/DZXB.20240024

LIAO Lei-yao. High-Range Resolution Radar Target Recognition Based on Deep Interpretable Probabilistic Auto-Encoder Model[J]. Acta Electronica Sinica, 2024, 52(11): 3847-3857. DOI：10.12263/DZXB.20240024

摘要

现有基于深度神经网络的高距离分辨（High Range Resolution，HRR）雷达目标识别方法是纯数据驱动模型，是1个飞行事故记录器结构，特征不具可解释性，在方位缺失情况下特征泛化性差，模型识别率急剧下降.对此，本文设计了一种物理可解释自编码模型（Physical Interpretable Auto-Encoder Model，PIAEM），解码网络结合雷达目标的散射点模型，利用编码网络挖掘雷达数据具有明确物理含义的散射中心特征，从成像角度描述目标的物理结构特性，如尺寸、轮廓等，在方位缺失情况下具有稳健的物理特性.设计了基于最小重构误差的分类准则，实现雷达目标识别.基于实测高距离分辨雷达回波数据的实验结果表明，本文方法提取的特征具有明确物理含义，且在方位缺失4/5的情况下，PIAEM比现有基于传统目标识别方法的准确率提升了10.27%，验证了本文方法具有方位稳健识别性能.

Abstract

Existing neural network-based high range resolution (HRR) radar target recognition methods are data-driven models that are of black-box structure

which makes it hard to interpret or assess the hidden representations of data. In the case of incomplete target-aspect

neural network-based methods are faced with the issues of poor feature generalization ability and rapid degradation of recognition performance. To access the issues

this paper develops a physical interpretable auto-encoder model (PIAEM). In detail

by incorporating the scattering center model of radar targets into networks

the PIAEM is a physical interpretable model that learns scattering center features with physical meanings. Specially

since the scattering center features reflect the target structure based on radar imaging theory

they are robust under the case of incomplete target-aspect. Moreover

this paper designs a recognition scheme to predict the category of test samples based on the minimum reconstruction error criterion. The experiments on the measured HRR radar dataset validate the effectiveness of our model on learning interpretable features and robust recognition performance

and our PIAEM improves 10.27% rates comparing with traditional radar target recognition methods.

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references

翟颖 , 陈渤 . 基于稳健变分自编码模型的雷达高分辨距离像目标识别算法 [J ] . 电子学报 , 2020 , 48 ( 6 ): 1149 - 1155 .

ZHAI Y , CHEN B . Robust variational auto-encoder for radar HRRP target recognition [J ] . Acta Electronica Sinica , 2020 , 48 ( 6 ): 1149 - 1155 . (in Chinese)

张云 , 化青龙 , 姜义成 , 等 . 基于混合型复数域卷积神经网络的三维转动舰船目标识别 [J ] . 电子学报 , 2022 , 50 ( 5 ): 1042 - 1049 .

ZHANG Y , HUA Q L , JIANG Y C , et al . Recognition of 3D rotating ship based on mix-CV-CNN [J ] . Acta Electronica Sinica , 2022 , 50 ( 5 ): 1042 - 1049 . (in Chinese)

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) .

DU L , LIU H , BAO Z , et al . Radar automatic target recognition using complex high-resolution range profiles [J ] . IET Radar , Sonar & Navigation, 2007 , 1 ( 1 ): 18 .

BO F , DU L , LIU H W , et al . Radar HRRP target recognition based on K-SVD algorithm [C ] // Proceedings of 2011 IEEE CIE International Conference on Radar . Piscataway : IEEE , 2011 : 642 - 645 .

DU L , LIU H W , WANG P H , et al . Noise robust radar HRRP target recognition based on multitask factor analysis with small training data size [J ] . IEEE Transactions on Signal Processing , 2012 , 60 ( 7 ): 3546 - 3559 .

FENG B , CHEN B , LIU H W . Radar HRRP target recognition with deep networks [J ] . Pattern Recognition , 2017 , 61 : 379 - 393 .

DU C , CHEN B , XU B , et al . Factorized discriminative conditional variational auto-encoder for radar HRRP target recognition [J ] . Signal Processing , 2019 , 158 : 176 - 189 .

XU B , CHEN B , WAN J W , et al . Target-aware recurrent attentional network for radar HRRP target recognition [J ] . Signal Processing , 2019 , 155 : 268 - 280 .

霍超颖 , 闫华 , 冯雪健 , 等 . HRRP稀疏自编码器深层特征与散射中心特征的关联性研究 [J ] . 系统工程与电子技术 , 2021 , 43 ( 11 ): 3040 - 3053 .

HUO C Y , YAN H , FENG X J , et al . Correlation research between deep features of HRRP sparse auto-encoder and scattering center features [J ] . Systems Engineering and Electronics , 2021 , 43 ( 11 ): 3040 - 3053 . (in Chinese)

DE FREITAS A , DE VILLIERS J P . Multiple scatterer tracking in high range resolution radar [C ] // 2012 15th International Conference on Information Fusion . Piscataway : IEEE , 2012 : 1683 - 1688 .

KELLER J B . Geometrical theory of diffraction [J ] . Journal of the Optical Society of America , 1962 , 52 ( 2 ): 116 - 130 .

DU L , HE H , ZHAO L , et al . Noise robust radar HRRP target recognition based on scatterer matching algorithm [J ] . IEEE Sensors Journal , 2016 , 16 ( 6 ): 1743 - 1753 .

LIAO L Y , DU L , CHEN J . Class factorized complex variational auto-encoder for HRR radar target recognition [J ] . Signal Processing , 2021 , 182 : 107932 .

杜兰 . 雷达高分辨距离像目标识别方法研究 [D ] . 西安 : 西安电子科技大学 , 2007 .

DU L . Study on Radar HRRP Target Recognition [D ] . Xi'an : Xidian University , 2007 .

LI C , DU L , DENG S , et al . Point-wise discriminative auto-encoder with application on robust radar automatic target recognition [J ] . Signal Processing , 2020 , 169 : 107385 .

HU J , DU L , HE H , et al . Noise-robust radar HRR target recognition based on complex Gaussian statistical models [C ] // 2016 CIE International Conference on Radar (RADAR) . Piscataway : IEEE , 2016 : 1 - 4 .

LI J , STOICA P . Efficient mixed-spectrum estimation with applications to target feature extraction [J ] . IEEE Transactions on Signal Processing , 1996 , 44 ( 2 ): 281 - 295 .

YU H , YANG J . A direct LDA algorithm for high-dimensional data—With application to face recognition [J ] . Pattern Recognition , 2001 , 34 ( 10 ): 2067 - 2070 .

CHANG C C , LIN C J . LIBSVM: A library for support vector machines [J ] . ACM Transactions on Intelligent Systems and Technology , 2011 , 2 ( 3 ): 27 .

DU L , WANG P H , ZHANG L , et al . Robust statistical recognition and reconstruction scheme based on hierarchical Bayesian learning of HRR radar target signal [J ] . Expert Systems with Applications , 2015 , 42 ( 14 ): 5860 - 5873 .

VINCENT P , LAROCHELLE H , LAJOIE I , et al . Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion [J ] . Journal of Machine Learning Research , 2010 , 11 : 3371 - 3408 .

CHEN W C , CHEN B , PENG X J , et al . Tensor RNN with Bayesian nonparametric mixture for radar HRRP modeling and target recognition [J ] . IEEE Transactions on Signal Processing , 2021 , 69 : 1995 - 2009 .

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