基于压缩感知的条带SAR缺失数据恢复成像方法  

Recovery and imaging method for missing data of the strip-map SAR based on compressive sensing

作　　者：段化军[1] 朱岱寅[1] 李勇 吴迪[1] 

机构地区：[1]南京航空航天大学电子信息工程学院雷达成像与微波光子技术教育部重点实验室,江苏南京210016

出　　处：《系统工程与电子技术》2016年第5期1025-1031,共7页Systems Engineering and Electronics

基　　金：国家自然科学基金(61301212);航空科学基金(20132052030;20142052020);中央高校基本科研业务费专项资金(NP2015504);中国博士后科学基金(2012M511750);国防基础科研计划(B2520110008);江苏省研究生培养创新工程(SJLX_0131);江苏高校优势学科建设工程资助课题

摘　　要：针对条带模式合成孔径雷达回波缺失数据,提出了一种利用压缩感知恢复缺失数据并成像的方法。将条带数据分块为多个子孔径数据,对子孔径利用压缩感知恢复缺失数据并拼接得到条带数据,缩短了整个数据的恢复时间,推导了压缩感知处理的基矩阵和测量矩阵。运用最大似然估计的特征向量方法(eigenvector method for maximum likelihood estimation,EMMLE)实现了子孔径缺失数据的自聚焦,满足了压缩感知对图像的稀疏要求。利用压缩感知恢复完整的相位误差信号,解决了子孔径补偿相位误差数据的拼接问题。最后通过对恢复的雷达回波数据成像并自聚焦校正了距离徙动,得到了聚焦良好的完整图像,提高了缺失数据的成像质量。A recovery and imaging method for missing data of the strip-map mode synthetic aperture radar （SAR） based on compressive sensing （CS） is introduced. The strip-map data is segmented into several sub-ap- ertures, which results in reducing the recovery time significantly. The sub-aperture missing data can be restored by CS and be stitched to the strip-map data. The basis matrix and the measurement matrix for CS are proposed. The sub-aperture data are autofocused by the eigenvector method for maximum-likelihood estimation to meet the sparse requirement of the reconstructed image and the intact phase error data is restored by CS in order to stitch the sub-aperture. A high quality image of the restored data can be obtained by the conventional imaging method and autofocus which corrects the range migration.

关 键 词：合成孔径雷达 压缩感知 最大似然估计的特征向量方法 数据恢复 

分 类 号：TN958[电子电信—信号与信息处理]