基于特征增强的非均匀采样SAR三维稀疏成像

对于非均匀采样数据, 现有三维稀疏成像方法中的数据局部插值会带来误差, 且得到的分布式目标成像结果与其连续散射的本质不符。针对这些问题, 首先将成像问题直接建模为三维空间中联合的稀疏重构问题, 并通过选取候选散射中心进行字典降维; 然后, 在降维后的模型中增加三维特征增强约束项, 建立三维空间中相邻散射中心之间的联系; 最后, 结合高斯迭代法以及优化的信号处理技巧, 提出了一种高效的模型求解算法。实验结果表明, 相比于其他成像方法, 本文方法对旁瓣的抑制能力强, 成像结果分辨率高、精度高, 且保证了分布式目标成像结果的连续性。

3D sparse imaging for non-uniformly sampled SAR based on feature enhancement

For non-uniform sampling data,the local data interpolation in the existing three-dimensional(3D)sparse imaging methods will bring errors,and the imaging result of distributed targets is poor.To solve these problems,firstly,the imaging problem is directly modeled as a joint sparse reconstruction problem in 3D space,and the dictionary reduction is carried out by selecting the candidate scattering centers;secondly,the 3D feature enhancement constraints are added to the reduced model to establish the relationship between adjacent scattering centers in 3D space;finally,an efficient model solving algorithm is proposed by combining the Gaussian iterative method and the optimized signal processing scheme.The experimental results show that,compared with other imaging methods,the proposed method has a strong ability to suppress side lobe,whose imaging results are with high resolution and high precision,and performs well on distributed targets.