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刘发林

博士生导师
硕士生导师
教师姓名:刘发林
教师英文名称:LIU Falin
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学历:博士研究生毕业
联系方式:0551-63601922
学位:工学博士学位
职称:研究员
毕业院校:中国科学技术大学
所属院系:信息科学技术学院
学科:电子科学与技术    信息与通信工程    
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论文成果
A Robust Maximum a Posteriori Algorithm for One-Bit Synthetic Aperture Radar Imaging
发布时间:2024-07-21    点击次数:

DOI码:10.1109/TGRS.2024.3431204

所属单位:中国科学技术大学信息学院

教研室:电子工程与信息科学系

发表刊物:IEEE Transactions on Geoscience and Remote Sensing (Early Access )

关键字:One-bit SAR imaging, robust maximum a posteriori(RMAP), sparse signal reconstruction.

摘要:Over the past decade, extensive research has been conducted on one-bit synthetic aperture radar (SAR) imaging algorithms. These algorithms aim to replace high-speed and high-precision analog-to-digital converters (ADCs) with comparators for low cost, noise robustness, and lightened transmission and storage burdens. The conventional maximum a posteriori (MAP) approach is commonly employed for reconstructing sparse scenes in SAR imaging. It can effectively reconstruct scenes containing discrete targets with relatively high quality under sufficiently low signal-to-noise ratio (SNR) conditions. However, when SNR increases, this algorithm has limitations as it cannot significantly improve imaging results. Furthermore, when it comes to reconstructing continuous targets, the imaging quality is notably poor. To address these challenges, this paper proposes a robust maximum a posteriori (RMAP) algorithm. It reconstructs sparse scenes by optimizing a convex function derived from the bound optimization approach and employing the adaptive gradient descent method. And a vital approximation is utilized to prominently improve the stability of the algorithm and it can be generally extended to other related imaging methods as long as the ratio of the cumulative distribution function (CDF) and the probability density distribution (PDF) of the standard normal distribution is involved. The results of both simulation and real data experiments validate the effectiveness of the RMAP algorithm. It accurately reconstructs sparse scenes in low SNR conditions and exhibits superior robustness compared to alternative methods such as the MAP, binary iterative hard threshold (BIHT), and adaptive outlier pursuit (AOP) algorithms.

合写作者:Mingyu Niu,Fangxi Liu

第一作者:Ruirui Wu(吴瑞瑞)

论文类型:期刊论文

通讯作者:Falin Liu

论文编号:10.1109/TGRS.2024.3431204

学科门类:工学

文献类型:J

卷号:online

期号:online

页面范围:1-13

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发表时间:2024-07-19

收录刊物:SCI