基于Lasso的稀疏子块微波成像研究

Q2 Physics and Astronomy
Xiang Yin, Zhang Bing-chen, H. Wen
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引用次数: 0

摘要

稀疏微波成像需要一种非线性算法,对于大场景成像来说,这是一种昂贵的算法。为此,研究了将实测数据和相对成像区域划分为子块的子块成像方法。然后,对每个子块执行基于最小绝对收缩和选择算子(Lasso)的稀疏微波成像算法。最后,将子块进行组合,得到大场景的整体图像。与稀疏场景的整体重构相比,子块算法可以控制每次重构所涉及的数据量,从而避免了信号处理器频繁访问磁盘的耗时问题。进一步,理论分析表明,子块稀疏成像方法也具有较好的准确性和稳定性,相关重建误差不超过整体重建误差的2倍。仿真和实际数据处理结果验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study on the Sparse Sub-block Microwave Imaging Based on Lasso
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.
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来源期刊
雷达学报
雷达学报 Physics and Astronomy-Instrumentation
CiteScore
4.10
自引率
0.00%
发文量
882
期刊介绍: Information not localized
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