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。
Sparse microwave imaging requires a nonlinear algorithm that is expensive for large scene imaging. Therefore, the sub-block imaging method, in which the measured data and the relative imaging region are divided into sub-blocks, is studied. Then, a sparse microwave imaging algorithm based on the 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. When compared with the overall reconstruction of the sparse scene, the sub-block algorithm can control the amount of data involved in each reconstruction, thereby avoiding frequent accessing of the disk by the signal processor, which is time consuming. Further, 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 that of the overall reconstruction. The simulation and real data processing results support the validity of our method.
出处
《雷达学报（中英文）》
CSCD
2013年第3期271-277,共7页
Journal of Radars
基金
Supported by the National Research Program of China(No.2010CB731905)
关键词
微波成像
稀疏信号处理
稀疏微波成像
Lasso
分块成像
Microwave imaging
Sparse signal processing
Sparse microwave imaging
Least absolute shrinkage andselection operator （Lasso）
Sub-block imaging
