电子学报 ›› 2017, Vol. 45 ›› Issue (4): 874-881.DOI: 10.3969/j.issn.0372-2112.2017.04.016

• 学术论文 • 上一篇    下一篇

基于压缩感知的矩阵型联合SAR成像与自聚焦算法

卜红霞1, 白霞2, 赵娟2, 齐耀辉1, 闫若颖1   

  1. 1. 河北师范大学物理科学与信息工程学院, 河北石家庄 050024;
    2. 北京理工大学信息与电子学院, 北京 100081
  • 收稿日期:2015-12-21 修回日期:2016-09-19 出版日期:2017-04-25
    • 通讯作者:
    • 卜红霞
    • 作者简介:
    • 白霞 女,1978年出生于辽宁,2006年于北京航空航天大学电子信息工程学院获博士学位,2006-2008年于北京航空航天大学从事博士后研究工作,现为北京理工大学教师,硕士生导师.主要从事雷达信号处理和压缩感知等方面的研究工作.E-mail:bai@bit.edu.cn
    • 基金资助:
    • 河北省高等学校自然科学重点项目 (No.ZD2016031); 河北师范大学自然科学科研基金 (No.L2016B06,No.L2010Y01)

Joint Matrix Form SAR Imaging and Autofocus Based on Compressed Sensing

BU Hong-xia1, BAI Xia2, ZHAO Juan2, QI Yao-hui1, YAN Ruo-ying1   

  1. 1. College of Physics Science and Information Engineering, Hebei Normal University, Shijiazhuang, Hebei 050024, China;
    2. School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
  • Received:2015-12-21 Revised:2016-09-19 Online:2017-04-25 Published:2017-04-25
    • Supported by:
    • Natural Science Key Program of Colleges and Universities in Hebei Province (No.ZD2016031); Natural Science Research Fund of Hebei Normal University (No.L2016B06, No.L2010Y01)

摘要:

模型准确情况下,压缩感知在合成孔径雷达成像中得到良好应用;但在实际情况中,模型会存在一定误差,这些误差造成图像偏离真实位置、引起散焦降低成像质量.本文提出一种矩阵型联合CS-SAR成像与自聚焦算法,该算法在CS-SAR成像重构方法方面,基于光滑l0范数方法提出了矩阵型正则化光滑l0范数重构方法,该方法具有较强容错能力并能直接重构矩阵型信号,能克服现有联合CS-SAR成像与自聚焦算法在计算效率方面的缺陷.最后,通过仿真验证了所提算法的有效性.

关键词: 合成孔径雷达, 压缩感知, 光滑l0范数重构算法, 矩阵型正则化光滑l0范数重构算法

Abstract:

Compressed sensing (CS) has been successfully applied to the synthetic aperture radar (SAR) imaging.These CS-based SAR imaging algorithms generally assume that the model of the imaging system is accurate.However,in practice it is common to encounter model errors which usually introduce unknown phase errors into the acquired data.The phase errors may cause range migration or defocusing.In this paper,an approach for matrix form joint CS-SAR imaging and autofocus is proposed.Based on smoothed l0 norm (SL0) algorithm,we develop a matrix form regularized SL0 (MRSL0) algorithm to efficiently perform CS-SAR imaging.The MRSL0 adopts inequality constrain to tolerate phase errors and has fast computation speed due to its matrix form.Experiment results demonstrate that the proposed approach can efficiently reconstruct high quality images using limited amount of measurements.

Key words: synthetic aperture radar (SAR), compressed sensing (CS), smoothed l 0-norm (SL0) algorithm, matrix form regularized SL0 (MRSL0) algorithm

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