CAJ | 학술논문

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。
희소미파성상수요사용상대복잡적비선성처리방법，저사방법난우처리대장경성상문제，위차，해문제출료일충괄용우대장경희소미파성상적분괴성상방법。해방법수선장대장경관측수거화성상구역분할성일일대응적자수거괴화자구역，연후이용기우Lasso적희소미파성상방법대각자구역독립중건，최후병접자구역중건결과득도대장경정체도상。상비우대희소관측장경진행정체중건，해분괴처리방법가이공제매차중건소섭급적수거량，동시이론분석표명분괴처리희소장경중건오차불초과정체중건오차상계적량배。수치방진급실측수거처리결과험증료해분괴처리방법적유효성。
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.