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

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

1.
西安电子科技大学雷达信号处理国家重点实验室西安 710071
2.
西安电子科技大学前沿交叉研究院西安 710071
3.
西安电子科技大学空间科学与技术学院西安 710071

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
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- 被引次数: 0
出版历程
- 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

Electromagnetic Scattering Characteristic Extraction and Imaging Recognition Algorithm: A Review

1.
National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
2.
Academy of Advanced Interdisciplinary Research, Xidian University, Xi’an 710071, China
3.
School of Aerospace Science And Technology, Xidian University, Xi’an 710071, China

Funds: The National Natural Science Foundation of China for Distinguished Young Scholars (61825105)

More Information

Corresponding author: XING Mengdao, xmd@xidian.edu.cn

摘要

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。
- 合成孔径雷达 /
- 电磁散射特征 /
- 参数估计 /
- 雷达成像算法 /
- 目标识别
Abstract: One remarkable trend in applying synthetic aperture radar technology is the automatic interpretation of Synthetic Aperture Radar (SAR) images. The electromagnetic scattering characteristics have a robust correlation with the target structure, which provides key support for SAR image interpretation. Therefore, elucidating how to extract accurate electromagnetic characteristics and how to use these electromagnetic characteristics to retrieve target characteristics has been widely valued recently. This study discusses the research accomplishments, summarizes the key elements and ideas of electromagnetic characteristic extraction and electromagnetic-characteristic-based target recognition, and details the extension applications of the electromagnetic scattering mechanism in imaging and recognition. Finally, the future research direction of electromagnetic scattering characteristic extraction and application was proposed.
- Synthetic Aperture Radar (SAR) /
- Electromagnetic scattering characteristic /
- Parameter estimation /
- Radar imaging algorithm /
- Target recognition

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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

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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

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图 3 FEKO仿真模型和成像结果

Figure 3. Experiment on simulation model by FEKO

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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]

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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]

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图 6 坦克模型

Figure 6. Tank model

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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

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图 8 挖掘机极化伪彩合成结果

Figure 8. The pseudocolor imaging results of XPATCH backhoe

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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

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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

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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]

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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]

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表 1 典型散射体结构的$\alpha $和$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	帽
0	=0	球
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

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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
参数	1	2
$L\left( {\rm{m}} \right)$	1.6	/
$\bar \varphi \left( {{\rm{rad}}} \right)$	0	/
$x\left( {\rm{m}}\right)$	–0.1	2.7756e–17
$y\left( {\rm{m}} \right)$	0	2.0
$\alpha $	0.5	0
复幅度A	2.4570–0.3162i	2.9605+0.3071i
散射体结构	圆柱体	球体

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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	SOC	EOC
BFM	95.62	97.90
SVM	96.78	96.64
ACN	99.13	98.12
SRC	96.29	95.12
FGL	99.08	98.46
所提方法	99.11	98.37

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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	SOC	EOC
BFM	95.62	97.90
SVM	96.78	96.64
ACN	99.13	98.12
SRC	96.29	95.12
FGL	99.08	98.46
所提方法	99.59	98.48

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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	94.3	91.8
Gabor滤波器	94.2	92.3
所提方法	98.8	95.1

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