Study on the Sparse Sub-block Microwave Imaging Based on Lasso(In English)
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摘要: 稀疏微波成像需要使用相对复杂的非线性处理方法,这些方法难于处理大场景成像问题,为此,该文提出了一种适用于大场景稀疏微波成像的分块成像方法。该方法首先将大场景观测数据和成像区域分割成一一对应的子数据块和子区域,然后利用基于Lasso 的稀疏微波成像方法对各子区域独立重建,最后拼接子区域重建结果得到大场景整体图像。相比于对稀疏观测场景进行整体重建,该分块处理方法可以控制每次重建所涉及的数据量,同时理论分析表明分块处理稀疏场景重建误差不超过整体重建误差上界的两倍。数值仿真及实测数据处理结果验证了该分块处理方法的有效性。Abstract: Sparse microwave imaging requires nonlinear algorithm that is expensive for large scene imaging. Therefore, the sub-block imaging method is studied, in which the measured data and the relative imaging region is divided into sub-blocks, and then sparse microwave imaging algorithm based on Least absolute shrinkage and selection operator (Lasso) is performed on each sub-block, finally the sub-blocks are combined to obtain the whole image of the large scene. Compared to the overall reconstruction of the sparse scene, sub-block algorithm can control data amount involved in each reconstruction, so as to avoid the signal processor frequently accessing the disk, which will cost huge time. Indeed, the theoretical analysis illustrates that the sub-block sparse imaging method is also accurate and stable, and the associated reconstruction error is no more than two times of that of the overall reconstruction. The result proved by simulation and real data processing supports the validity of our method.
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