电磁散射特征提取与成像识别算法综述

邢孟道 谢意远 高悦欣 张金松 刘嘉铭 吴之鑫

邢孟道, 谢意远, 高悦欣, 等. 电磁散射特征提取与成像识别算法综述[J]. 雷达学报, 2022, 11(6): 921–942. doi: 10.12000/JR22232
引用本文: 邢孟道, 谢意远, 高悦欣, 等. 电磁散射特征提取与成像识别算法综述[J]. 雷达学报, 2022, 11(6): 921–942. doi: 10.12000/JR22232
XING Mengdao, XIE Yiyuan, GAO Yuexin, et al. Electromagnetic scattering characteristic extraction and imaging recognition algorithm: a review[J]. Journal of Radars, 2022, 11(6): 921–942. doi: 10.12000/JR22232
Citation: XING Mengdao, XIE Yiyuan, GAO Yuexin, et al. Electromagnetic scattering characteristic extraction and imaging recognition algorithm: a review[J]. Journal of Radars, 2022, 11(6): 921–942. doi: 10.12000/JR22232

电磁散射特征提取与成像识别算法综述

doi: 10.12000/JR22232
基金项目: 国家自然科学基金杰出青年基金(61825105)
详细信息
    作者简介:

    邢孟道,教授,博士生导师,主要研究方向为雷达成像技术、稀疏信号处理、激光合成孔径成像、微波光子合成孔径成像和雷达遥感技术

    谢意远,博士研究生,主要研究方向为雷达目标电磁特征提取与重构等

    高悦欣,博士,讲师,主要研究方向为ISAR高分辨成像等

    张金松,博士,讲师,主要研究方向为SAR 目标检测与识别等

    刘嘉铭,博士研究生,主要研究方向为ISAR 目标检测与识别等

    吴之鑫,博士研究生,主要研究方向为雷达目标电磁建模等

    通讯作者:

    邢孟道 xmd@xidian.edu.cn

  • 责任主编:文贡坚 Corresponding Editor: WEN Gongjian
  • 中图分类号: TN957.52

Electromagnetic Scattering Characteristic Extraction and Imaging Recognition Algorithm: A Review

Funds: The National Natural Science Foundation of China for Distinguished Young Scholars (61825105)
More Information
  • 摘要: 合成孔径雷达(SAR)图像的自动化解译是合成孔径雷达技术应用的重要发展方向之一。电磁散射特征与目标结构具有稳健的关联性,是SAR图像解译的关键支撑。近年来,如何准确地实现电磁特征提取以及利用电磁特征反演目标特性受到了广泛的重视。该文讨论了电磁特征提取和基于电磁散射特征识别方法的研究成果,总结归纳了其中的关键要素和主要思路,详述了电磁散射机理在成像和识别领域的扩展应用,展望了未来电磁散射特征提取与应用的研究发展趋势。

     

  • 图  1  电磁特征在SAR成像体系中的支撑作用示意图

    Figure  1.  The supporting role of electromagnetic scattering characteristic in SAR imaging

    图  2  电磁散射特征提取方法与应用的主要特征概述

    Figure  2.  The overview of main features of electromagnetic scattering characteristic extraction and application

    图  3  FEKO仿真模型和成像结果

    Figure  3.  Experiment on simulation model by FEKO

    图  4  文献[47]所提方法的XPATCH仿真数据提取实验结果

    Figure  4.  Imaging results obtained by the proposed method in Ref. [47]

    图  5  文献[47]所提方法的实测数据提取实验结果

    Figure  5.  Imaging results obtained by the proposed method in Ref. [47]

    图  6  坦克模型

    Figure  6.  Tank model

    图  7  文献[51]所提方法的坦克数据提取实验结果

    Figure  7.  Imaging results obtained by the proposed method in Ref. [51] on tank model

    图  8  挖掘机极化伪彩合成结果

    Figure  8.  The pseudocolor imaging results of XPATCH backhoe

    图  9  文献[80]所提方法与传统PTD方法的XPATCH仿真数据PTD结果对比分析

    Figure  9.  PTD performance comparison by the proposed method in Ref. [80] and the traditional method

    图  10  文献[96]所提方法和Burg算法的坦克仿真数据不同倍数超分辨成像对比

    Figure  10.  Super-resolution performance comparison by the proposed method in Ref. [96] and the Burg method

    图  11  文献[114]所提融合识别网络结构示意图

    Figure  11.  Proposed recognition method fusing scattering center features and CNN features at the level of feature maps in Ref. [114]

    图  12  文献[115]所提融合电磁散射特征向量和深度CNN特征向量的识别方法

    Figure  12.  Structure of the proposed FEC framework in Ref. [115]

    表  1  典型散射体结构的$\alpha $$L $

    Table  1.   Canonical scattering geometries from $\alpha $ and $L $

    $\alpha $L散射体结构
    1.0=0三面角
    0.5=0
    0=0
    1.0>0二面角
    0.5>0圆柱
    0>0直边
    –0.5>0边缘绕射
    –1.0=0角衍射
    下载: 导出CSV

    表  2  文献[42]所提方法的FEKO模型参数估计结果

    Table  2.   Estimated parameters result on FEKO model by the proposed method in Ref. [42]

    参数散射中心
    12
    $L\left( {\rm{m}} \right)$1.6/
    $\bar \varphi \left( {{\rm{rad}}} \right)$0/
    $x\left( {\rm{m}}\right)$–0.12.7756e–17
    $y\left( {\rm{m}} \right)$02.0
    $\alpha $0.50
    复幅度A2.4570–0.3162i2.9605+0.3071i
    散射体结构圆柱体球体
    下载: 导出CSV

    表  3  文献[114]所提方法和其他典型方法的识别准确度

    Table  3.   Performance of the proposed method in Ref. [114] and other different methods

    方法类型SOCEOC
    BFM95.6297.90
    SVM96.7896.64
    ACN99.1398.12
    SRC96.2995.12
    FGL99.0898.46
    所提方法99.1198.37
    下载: 导出CSV

    表  4  文献[115]方法和其他典型方法的识别准确度

    Table  4.   Performance of the proposed method in Ref. [115] and other different methods

    方法类型SOCEOC
    BFM95.6297.90
    SVM96.7896.64
    ACN99.1398.12
    SRC96.2995.12
    FGL99.0898.46
    所提方法99.5998.48
    下载: 导出CSV

    表  5  文献[116]方法和其他参数初始化方法的识别准确度

    Table  5.   Performance of the proposed methed in Ref. [116] and other different methods

    方法类型俯仰角15°俯仰角17°
    随机初始化94.391.8
    Gabor滤波器94.292.3
    所提方法98.895.1
    下载: 导出CSV
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  • 收稿日期:  2022-11-30
  • 修回日期:  2022-12-22
  • 网络出版日期:  2022-12-27
  • 刊出日期:  2022-12-28

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