低信噪比下基于Hough变换的前视阵列SAR稀疏三维成像

刘向阳; 杨君刚; 孟进; 张晓; 牛德智

doi:10.12000/JR17011

低信噪比下基于Hough变换的前视阵列SAR稀疏三维成像

DOI: 10.12000/JR17011

西安通信学院西安 710106

基金项目: 国家自然科学基金(61640006)，陕西省自然科学基金(2015JM6307)

详细信息

作者简介:
刘向阳(1982–)，男，河北石家庄人，2010年在西安电子科技大学获得信息与通信工程专业博士学位，现为西安通信学院讲师、教研室副主任，主要研究方向为前视阵列SAR、回波建模与仿真、动目标检测。

杨君刚(1973–)，男，陕西宝鸡人，2008年在西安电子科技大学获得信息与通信工程专业博士学位，现为西安通信学院教授、系主任，主要研究方向为信号检测与估计、通信信号处理

孟进：孟进(1975–)，男，陕西宝鸡人，2003年在信息工程大学获得硕士学位，现为西安通信学院副教授，主要研究方向为信号检测与估计、通信网管理

通讯作者:
刘向阳 xiangyangliu@mail.xidian.edu.cn

中图分类号: TN959.3
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出版历程
- 收稿日期: 2017-01-23
- 修回日期: 2017-06-05
- 网络出版日期: 2017-06-28

Sparse Three-dimensional Imaging Based on Hough Transform for Forward-looking Array SAR in Low SNR

Xi’an Communications Institute, Xi’an710106, China

Funds: The National Natural Science Foundation of China (61640006), The Natural Science Foundation of Shaanxi Province (2015JM6307)

摘要

摘要: 低信噪比下压缩感知稀疏重构的性能不佳，使得前视阵列SAR稀疏3维成像的质量大大降低。为此，该文结合散射系数矢量在原始的距离向和斜距离向2维空间中的连续性特征，借鉴信号检测中常用的直线检测思路和方法，提出基于霍夫变换的压缩感知稀疏重构算法，有效提高压缩感知稀疏重构的性能。同时，仿真实验结果表明，该方法有效实现了低信噪比下前视阵列SAR的稀疏3维成像。
- 前视阵列SAR /
- 3维成像 /
- 压缩感知 /
- 稀疏成像 /
- 霍夫变换
Abstract: The performance of sparse reconstruction algorithm of compressive sensing in low Signal-to-Noise Ratio (SNR) is lower, and the quality of sparse three-dimensional imaging for forward-looking array synthetic aperture radar in low SNR is reduced greatly. To solve this problem, a validate method of reconstruction algorithm of compressive sensing based on Hough transform is proposed, in which the continuity of the scattering coefficient vector in the two-dimensional space of range direction and slant range direction and the straight line detection method of Hough transform is used, and thus the reconstruction quality of compressive sensing is increased effectively. Also, the simulation experiments indicate that this method can improve the sparse three-dimensional imaging for forward-looking array SAR in low SNR effectively.
- Forward-looking array Synthetic Aperture Radar (SAR) /
- Three-dimensional imaging /
- Compressive sensing /
- Sparse imaging /
- Hough transform

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图 1 前视阵列SAR对地观测模型

Figure 1. The ground observation model for forward-looking array SAR

下载: 全尺寸图片幻灯片

图 2 某个距离门散射源的空间位置示意图

Figure 2. The diagram of the space position of scatterers in a range cell

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图 3 前视阵列SAR稀疏3维成像流程图

Figure 3. Flow chart of sparse three-dimensional imaging for forward-looking array SAR

下载: 全尺寸图片幻灯片

图 4 3种压缩感知算法重构性能的比较

Figure 4. Reconstruction performance comparison of three compressive sensing algorithms

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图 5 水平地表的3维图像

Figure 5. Three-dimensional image of horizontal ground

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图 6 起伏地表的高度图

Figure 6. Height map of undulating ground

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图 7 起伏地形下的3维图像

Figure 7. Three-dimensional image of undulating ground

下载: 全尺寸图片幻灯片

表 1 前视阵列SAR系统仿真参数

Table 1. Simulation parameters for forward-looking array SAR system

参数	数值
中心波长(mm)	3
脉冲带宽(MHz)	100
脉冲时宽(μs)	1
采样频率(MHz)	120
合成总带宽(GHz)	1
平台速度(m/s)	50
场景中心斜距(km)	1
发射天线高度(m)	499.75
接收天线高度(m)	500.25
跨航向天线长度(m)	6
接收阵元间距(cm)	3
波束中心俯仰角(°)	60
波束中心方位角(°)	0
沿航向合成孔径长度(m)	30
子测绘带波束宽度(°)	3
距离向分辨率(m)	0.15
沿航向分辨率(m)	0.2
跨航向分辨率(m)	0.5

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参考文献(12)

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