田博坤,
张晓玲,
师君
电子科技大学电子工程学院 ??成都 ??611731
基金项目:国家自然科学基金(61501098)，博士后面上基金(2015M570778)，高分青年基金项目(GFZX04061502)，中央高校科研基本业务费(ZYGX2016KYQD107)
详细信息
作者简介:韦顺军(1983–)，男，广西柳州人，博士，2013年获电子科技大学工学博士学位，目前为电子科技大学信息与通信工程学院副教授，主要从事合成孔径雷达成像、阵列雷达3维成像等技术研究，已发表论文30余篇。E-mail: weishunjun@uestc.edu.cn
田博坤(1993–)，男，河北沧州人，电子科技大学博士生，主要从事合成孔径雷达成像研究。E-mail: 3544348143@qq.com
张晓玲(1964–)，女，四川成都人，博士，2002年获电子科技大学工学博士学位，目前为电子科技大学信息与通信工程学院教授，博士生导师，主要从事SAR成像技术、雷达探测技术研究，已发表论文50余篇。E-mail: xlzhang@uestc.edu.cn
师君：师 君(1979–)，男，河南南阳人，博士，2009年获电子科技大学工学博士学位，目前为电子科技大学信息与通信工程学院副教授，博士生导师，主要从事SAR成像技术、雷达信号处理研究，已发表论文50余篇。E-mail: shijun@uestc.edu.cn
通讯作者:韦顺军? weishunjun@uestc.edu.cn
中图分类号:TN957.52计量
文章访问数:1245
HTML全文浏览量:319
PDF下载量:233
被引次数:0
出版历程
收稿日期:2017-11-09
修回日期:2018-03-28
网络出版日期:2018-05-23
Compressed Sensing Linear Array SAR Autofocusing Imaging via Semi-definite Programming
Wei Shunjun,,Tian Bokun,
Zhang Xiaoling,
Shi Jun
School of Electronic Engineering University of Electronic Science and Technology of China, Chengdu 611731, China
Funds:The National Natural Science Foundation of China (61501098), The China Postdoctoral Science Foundation (2015M570778), The High Resolution Earth Observation Youth Foundation (GFZX04061502), The Fundamental Research Funds for the Central Universities (ZYGX2016KYQD107)
摘要
摘要:线阵合成孔径雷达(Linear Array Synthetic Aperture Radar, LASAR)3维成像技术是一种具有重要潜在应用价值的新体制成像雷达，压缩感知稀疏重构是近几年实现LASAR高分辨3维成像的热点研究之一。但相对于传统2维SAR，受线阵稀疏分布及阵列-平台2维联动，压缩感知LASAR成像面临回波数据欠采样、多维度高阶相位误差等问题，传统SAR自聚焦算法难以适用于压缩感知LASAR 3维稀疏自聚焦成像。为克服欠采样条件下多维度高阶相位误差对LASAR成像的影响，该文提出了一种基于半正定规划的压缩感知LASAR自聚焦成像算法。首先，结合压缩感知成像理论、图像最大锐度及最小均方误差准则，构造欠采样条件下稀疏目标的相位误差估计模型；其次，利用松弛半正定规划方法估计相位误差；最后，利用迭代逼近方法提高相位误差估计精度，实现压缩感知LASAR高精度稀疏自聚焦成像。另外，通过主散射目标区域提取，仅采用主散射区域进行相位误差估计，进一步提高自聚焦算法运算效率。仿真数据和实测数据验证了该文算法的有效性。
关键词:线阵SAR/
稀疏自聚焦成像/
最大锐度/
半正定规划/
压缩感知
Abstract:Linear Array Synthetic Aperture Radar (LASAR) is a novel and promising radar imaging technique. In recent years, Compressed Sensing (CS) sparse recovery has been a research focus for high-resolution three-Dimensional (3-D) LASAR imaging. Compared with the traditional two-Dimensional (2-D) SAR imaging, LASAR suffers from many problems, including under-sampling data and multi-dimensional and higher-order phase errors due to its sparse Linear Array Antenna (LAA) and the joint 2-D motions of the platform and LAA. The conventional autofocusing methods of 2-D SAR may be not suitable for CS-based LASAR 3-D sparse autofocusing. To address the multi-dimensional and higher-order phase errors in LASAR 3-D imaging with respect to under-sampling data, in this paper, we propose a sparse autofocusing algorithm based on semi-definite programming for CS-based LASAR imaging. First, by combining CS-based imaging theory, image maximum sharpness, and the minimum square error principle, we construct a LASAR phase-error estimation model based on under-sampled data. Next, we use semi-definite programming relaxation to estimate the phase errors. Lastly, we employ an iterated approximation method to improve the precision of the phase-error estimation and achieve the final CS-based LASAR autofocusing. To further improve the efficiency of the algorithm, we select only the dominant scattering areas for LASAR phase-error estimation. We present our simulation and experimental results to confirm the effectiveness of out proposed algorithm.
Key words:Linear Array Synthetic Aperture Radar (LASAR)/
Sparse autofocus imaging/
Maximum sharpness/
Semi-Definite Programming (SDP)/
Compressed Sensing (CS)
PDF全文下载地址:
https://plugin.sowise.cn/viewpdf/198_19ecbe27-555c-457a-95ca-332e0b2d2355_R17103
