稀疏场景下SAR方位向随机丢失数据的迭代成像算法

Iterative imaging algorithm for SAR azimuth random missing data with sparse scenes

针对合成孔径雷达(synthetic aperture radar,SAR)方位向随机丢失部分数据导致目标模糊和能量分散的问题,提出基于稀疏优化理论的重建成像方法。该方法主要针对稀疏观测场景的SAR方位向随机缺失数据的回波信号进行成像处理,利用SAR回波模拟算子,避免了二维回波信号矩阵变成向量的操作,从而减小了内存占用和计算量。所提出的基于SAR近似观测模型的迭代优化重建算法能够实现对观测目标幅度的高精度重建。和传统基于匹配滤波的SAR成像算法相比,提出的算法能够明显地消除SAR方位向随机丢失部分数据引起的目标模糊和目标能量分散。和迭代软阈值收缩算法相比,提出的算法重建的目标幅度误差更小。理想的点目标回波数据和真实的星载SAR稀疏观测场景的回波数据处理表明了所提算法在减少重建目标误差、提高观测目标区域的目标背景比等方面有较大的优势。

In order to solve the problem of target ambiguity and energy dispersion caused by the random missing data in synthetic aperture radar(SAR)azimuth,a novel reconstruction imaging method based on sparse optimization theory is proposed.This method mainly deals with the echo signal of SAR azimuth random missing data in sparse observation scene.The SAR echo simulation operator is used which can avoid the operation of two-dimensional echo signal matrix into vector and reduces complexity of memory consumption and computational.The iterative optimization reconstruction algorithm based on SAR approximate observation model can realize the high-precision reconstruction of the amplitude of the observed targets.Compared with the traditional SAR imaging algorithm based on matching filter,the proposed algorithm can obviously eliminate the targets ambiguity and targets energy dispersion caused by the random missing data in SAR azimuth.Compared with the iterative shrinkage thresholding algorithm,the proposed algorithm has smaller target amplitude error.The ideal point targets echo data and the real sparse observation scene spaceborne echo data processing show that the proposed algorithm has great advantages in reducing the reconstruction error of targets amplitude and improving the target background ratio in the observed targets area.