基于压缩感知的条带SAR缺失数据恢复成像方法

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。

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

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-apertures,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.