典型线面目标合成孔径雷达参数化成像

卫扬铠; 曾涛; 陈新亮; 丁泽刚; 范宇杰; 温育涵

doi:10.12000/JR19077

典型线面目标合成孔径雷达参数化成像

DOI: 10.12000/JR19077

卫扬铠^1,2,,
曾涛^1,2,3,,
陈新亮^1,3, ,,
丁泽刚^1,2,,
范宇杰^1,2,,
温育涵^1,2,

1.
北京理工大学信息与电子学院雷达技术研究所北京 100081
2.
嵌入式实时信息处理技术北京市重点实验室北京 100081
3.
北京理工大学重庆创新中心重庆 401120

基金项目: 国家重点研发计划(2017YFC0804700)，国家杰出青年基金(61625103)，国家自然科学基金重点项目(91738302, 91438203)

详细信息

作者简介:
卫扬铠(1995–)，男，湖北人。北京理工大学信号与信息处理专业博士研究生，主要研究方向为合成孔径雷达信号处理。E-mail: yangkai.wei@bit.edu.cn

曾　涛(1971–)，男，天津人。北京理工大学研究员、博士生导师，主要研究方向为雷达信息、信号处理与系统设计。E-mail: z.ding@bit.edu.cn

陈新亮(1984–)，男，湖北人。北京理工大学讲师，硕士生导师，主要研究方向为雷达信号检测与成像处理。E-mail: chenxinliang@bit.edu.cn

丁泽刚(1980–)，男，河南人。北京理工大学研究员、博士生导师，主要研究方向为新体制雷达成像机理、成像处理和图像信息提取。E-mail: z.ding@bit.edu.cn

范宇杰(1995–)，女，河北人。现北京理工大学信号处理专业硕士研究生，主要研究高精度分布式合成孔径雷达成像算法。E-mail: 18743052515@163.com

温育涵(1995–) ，男，北京人。北京理工大学信息与电子学院硕士研究生，主要研究方向为雷达信号处理及成像算法。E-mail: yuhan.wen@bit.edu.cn

通讯作者:
陈新亮 chenxinliang@bit.edu.cn

中图分类号: TN958
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出版历程
- 收稿日期: 2019-08-30
- 修回日期: 2019-12-04
- 网络出版日期: 2020-02-28

Parametric SAR Imaging for Typical Lines and Surfaces

WEI Yangkai^{1,2
,},
ZENG Tao^{1,2,3
,},
CHEN Xinliang^{1,3
, ,},
DING Zegang^{1,2
,},
FAN Yujie^{1,2
,},
WEN Yuhan^{1,2
,}

1.
Radar Research Lab, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
2.
Beijing Key Laboratory of Embedded Real-Time Processing Technology, Beijing 100081, China
3.
Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401120, China

Funds: The National key R & D plan (2017YFC0804700), The National Outstanding Youth Fund (61625103), The National Natural Science Foundation of China (91738302, 91438203)

More Information

Corresponding author: CHEN Xinliang, chenxinliang@bit.edu.cn

摘要

摘要: 在复杂场景(特别是城区场景)合成孔径雷达(SAR)遥感成像中，存在大量线、面目标，如城区中的道路和建筑物边缘等目标，这些线面目标微波散射信号方向性强。传统SAR从单一视角获取场景的散射信息，且传统成像算法均基于点目标模型，使得传统SAR图像中线面目标主要特征表现为一系列的强散射点，而非线散射特征和面散射特征，最终造成SAR图像中目标不连续，SAR图像解译困难。因此，该文通过建立典型线段、三角面元目标的参数化回波模型，对线面目标SAR成像机理进行了深入细致的研究；并基于提出的参数化模型对线面目标进行参数化成像，即首先基于贝叶斯理论和所提的参数化模型对典型的线面目标进行分类判决，随后采用再成像的方式获得有效表征线、面目标散射特征的SAR图像，为线、面目标SAR图像解译提供有效支撑。最后，数值仿真实验成功验证了所提算法的有效性和正确性。
- 线、面目标 /
- 合成孔径雷达 /
- 参数化模型 /
- 参数化成像
Abstract: In the Synthetic Aperture Radar (SAR) remote sensing imagery of complicated scenes (especially urban scenes), there are a large number of lines and surfaces, such as roads in urban areas and the surfaces of buildings. These microwave-signal-scattering features have strong directivity. Traditional SAR acquires the scattering information of a scene from a single observation, and traditional imaging algorithms are based on the point target model, which makes the main features of the lines and surfaces in traditional SAR images appear as a series of strong scattering points rather than line-scattering and surface-scattering features. This outcome ultimately causes the target to be discontinuous in the SAR image, thus making the SAR image difficult to interpret. Therefore, in this study, we conducted an in-depth and meticulous investigation of the SAR imaging mechanism for lines and surfaces by establishing a parametric echo model of typical lines and triangular surfaces. Based on the proposed parametric model, we performed parametric imaging of these lines and surfaces. Based on our results, we propose a parametric imaging method, in which the typical lines and surfaces are classified and determined based on Bayesian theory and the proposed parametric model. Then, an SAR image can be obtained that effectively characterizes the scattering features of the line and surface targets by visual imaging, which effectively facilitates SAR image interpretation. The results of our numerical simulation experiments verify the validity of the proposed method.
- Lines and surfaces /
- Synthetic Aperture Radar (SAR) /
- Parametric model /
- Parametric imaging

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图 1 直线参数化模型

Figure 1. Parametric line model

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图 2 线段连续与离散模型仿真对比

Figure 2. Simulation of continuous and discrete lines

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图 3 三角面元目标参数化模型

Figure 3. Triangular face parameterization model

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图 4 三角面元连续与离散模型仿真对比

Figure 4. Comparison of continuous and discrete models of triangular surface

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图 5 连续目标示意图与SAR成像示意图

Figure 5. Continuous target schematic and SAR imaging schematic

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图 6 不同角度采样下连续目标SAR成像意图

Figure 6. SAR imaging results of continuous target under different angles of sampling

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图 7 不同角度采样下三角面元SAR成像意图

Figure 7. SAR imaging results of triangular surface under different angle samplings

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图 8 雷达系统与信号频谱关系示意图

Figure 8. Schematic relationship between radar system and signal spectrum

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图 9 直线方位向成像模型图

Figure 9. Azimuth imaging model for line

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图 10 参数化成像流程图

Figure 10. Parametric imaging flowchart

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图 11 折线几何参数化图

Figure 11. Polyline geometry parameterization

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图 12 直线段可视化展示

Figure 12. Visualization of line

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图 13 三角面元可视化展示

Figure 13. Visualization of triangular surface

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表 1 雷达部分参数

Table 1. Some radar parameters

雷达参数	数值
波长	0.05 m
带宽	300 MHz
距离向频点数	60
方位角	0.075 rad
方位向脉冲数	360

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表 2 目标参数分布

Table 2. Target parameter distribution

目标类型	参数	参数分布
线段	长度L	截断高斯分布 $L \in \left[ {0.5,8} \right]$ , $\mu = 6,\varSigma = 1$
线段	法线角度 ${\varphi _n}$	$\left[ {0,2{\rm{\pi }}} \right]$ 均匀分布
折线	边长 ${L_1}$ , ${L_2}$	截断高斯分布 $L \in \left[ {0.1,3} \right]$ , $\mu = 2,\varSigma = 0.5$
折线	倾斜角度 ${\phi _l}$	$\left[ {0,2{\rm{\pi }}} \right]$ 均匀分布
三角形，三角面元	边长 ${L_1},{L_2},{L_3}$	截断高斯分布 $L \in \left[ {0.1,3} \right]$ , $\mu = 2,\varSigma = 0.5$
	倾斜角度 ${\phi _t}$	$\left[ {0,2{\rm{\pi }}} \right]$ 均匀分布

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表 3 基于贝叶斯理论的模型判决分类统计结果

Table 3. Statistical results of model decision classification based on Bayesian theory

输入目标(数量)	直线	折线	三角形	三角面元
直线(N=100)	87%	12%	1%
折线(N=100)	28%	64%	1%	7%
三角形(N=200)	7%	66.5%	26.5%	0
三角面(N=100)	0	0	0	100%

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表 4 直线段重构SAR参数

Table 4. Line: SAR parameters for reconstruction

雷达参数	具体数值
观测角度 $\tilde \theta$	80:0.01:110
重构带宽	3 GHz

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表 5 三角面元：重构SAR参数

Table 5. Triangular surface: SAR parameters for reconstruction

雷达参数	具体数值
观测角度 $\tilde \theta$	0.1:0.1:360
频率 $\tilde f$	25 MHz:20 MHz:6.125 GHz

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