Name
Abstract | ||
|---|---|---|
In recent years, the sparse signal processing technique has shown significant potential in synthetic aperture radar (SAR) imaging, such as image performance improvement and downsampled data-based image recovery. However, due to the huge computational complexity needed, the existing sparse SAR imaging methods, such as conventional observation matrix-based and azimuth-range decouple-based algorithms, are not able to achieve real-time processing, especially for the large-scale scenes, which seriously restricts its application in some fields, e.g., real-time monitoring and early warning. To solve this problem, this article presents a novel real-time sparse SAR imaging method, which can get a similar image performance to that obtained by the existing sparse imaging methods, to reduce the computational complexity to the same order as that required by matched filtering (MF)-based algorithms. This means that with the proposed method, real-time data processing for practical large-scale scene sparse reconstruction becomes possible. Experimental results based on simulated and real data along with a performance analysis are presented to validate the proposed real-time sparse imaging method. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TGRS.2019.2958067 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | DocType | Volume |
Azimuth-range decouple,L₁ regularization,real-time sparse imaging,synthetic aperture radar (SAR) | Journal | 58 |
Issue | ISSN | Citations |
4 | 0196-2892 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |