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2D normalized iterative hard thresholding algorithm for fast compressive radar imaging
Li GX(李恭新); Yang J(杨佳); Yang WG(杨文广); Wang YC(王越超); Wang WX(王文学); Liu LQ(刘连庆)
作者部门机器人学研究室
关键词Fast Compressive Radar Imaging Compressive Sensing Two Dimensional Normalized Iterative Hard Thresholding (2d-niht) Algorithm Compressive Radar Imaging Model Reconstruction Performance
发表期刊Remote Sensing
ISSN2072-4292
2017
卷号9期号:6页码:1-16
收录类别SCI ; EI
EI收录号20172603845516
WOS记录号WOS:000404623900110
产权排序1
资助机构National Natural Science Foundation of China (Grant No. 61327014, Grant No. 61433017) and the CAS/SAFEA International Partnership Program for Creative Research Teams.
摘要Compressive radar imaging has attracted considerable attention because it substantially reduces imaging time through directly compressive sampling. However, a problem that must be addressed for compressive radar imaging systems is the high computational complexity of reconstruction of sparse signals. In this paper, a novel algorithm, called two-dimensional (2D) normalized iterative hard thresholding (NIHT) or 2D-NIHT algorithm, is proposed to directly reconstruct radar images in the matrix domain. The reconstruction performance of 2D-NIHT algorithm was validated by an experiment on recovering a synthetic 2D sparse signal, and the superiority of the 2D-NIHT algorithm to the NIHT algorithm was demonstrated by a comprehensive comparison of its reconstruction performance. Moreover, to be used in compressive radar imaging systems, a 2D sampling model was also proposed to compress the range and azimuth data simultaneously. The practical application of the 2D-NIHT algorithm in radar systems was validated by recovering two radar scenes with noise at different signal-to-noise ratios, and the results showed that the 2D-NIHT algorithm could reconstruct radar scenes with a high probability of exact recovery in the matrix domain. In addition, the reconstruction performance of the 2D-NIHT algorithm was compared with four existing efficient reconstruction algorithms using the two radar scenes, and the results illustrated that, compared to the other algorithms, the 2D-NIHT algorithm could dramatically reduce the computational complexity in signal reconstruction and successfully reconstruct 2D sparse images with a high probability of exact recovery.
语种英语
WOS标题词Science & Technology ; Technology
WOS类目Remote Sensing
关键词[WOS]SPARSE DECOMPOSITION ; SIGNAL RECOVERY ; MATRICES
WOS研究方向Remote Sensing
引用统计
被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/20759
专题机器人学研究室
通讯作者Wang WX(王文学); Liu LQ(刘连庆)
作者单位1.State Key Laboratory of Robotics, Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang 110016, China
2.University of the Chinese Academy of Sciences, Beijing 100049, China
推荐引用方式
GB/T 7714
Li GX,Yang J,Yang WG,et al. 2D normalized iterative hard thresholding algorithm for fast compressive radar imaging[J]. Remote Sensing,2017,9(6):1-16.
APA Li GX,Yang J,Yang WG,Wang YC,Wang WX,&Liu LQ.(2017).2D normalized iterative hard thresholding algorithm for fast compressive radar imaging.Remote Sensing,9(6),1-16.
MLA Li GX,et al."2D normalized iterative hard thresholding algorithm for fast compressive radar imaging".Remote Sensing 9.6(2017):1-16.
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