RECENT PROGRESS IN SPARSITY-REGULARIZATION BASED IMAGING METHOD FOR NEAR-FIELD 3D SAR

被引：2

作者：

Zhan, Xu ^{[1
]}

Zhang, Xiaoling ^{[1
]}

Ma, Xiangdong ^{[1
]}

Wei, Shunjun ^{[1
]}

Shi, Jun ^{[1
]}

Zeng, Tianjiao ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Chengdu, Peoples R China

来源：

IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2023年

关键词：

3D Imaging; radar imaging; near-field; sparsity-regularization; deep learning; deep unfolding;

D O I：

10.1109/IGARSS52108.2023.10281465

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

Near-field three-dimensional synthetic aperture radar (Near-field 3D-SAR) is a powerful imaging technique with diverse applications in scattering diagnosis, person/parcel imaging, building monitoring, forest monitoring, and more. This paper provides an overview of the imaging methods employed in Near-field 3D-SAR, focusing on the underlying methodologies, imaging models, and solving flowcharts. By analyzing and summarizing these methodologies and flowcharts, valuable insights are uncovered. Additionally, potential research directions are identified for further exploration.

引用

页码：1802 / 1805

页数：4

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