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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)
Department机器人学研究室
Source PublicationRemote Sensing
ISSN2072-4292
2017
Volume9Issue:6Pages:1-16
Indexed BySCI ; EI
EI Accession number20172603845516
WOS IDWOS:000404623900110
Contribution Rank1
Funding OrganizationNational Natural Science Foundation of China (Grant No. 61327014, Grant No. 61433017) and the CAS/SAFEA International Partnership Program for Creative Research Teams.
KeywordFast Compressive Radar Imaging Compressive Sensing Two Dimensional Normalized Iterative Hard Thresholding (2d-niht) Algorithm Compressive Radar Imaging Model Reconstruction Performance
AbstractCompressive 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.
Language英语
WOS HeadingsScience & Technology ; Technology
WOS SubjectRemote Sensing
WOS KeywordSPARSE DECOMPOSITION ; SIGNAL RECOVERY ; MATRICES
WOS Research AreaRemote Sensing
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/20759
Collection机器人学研究室
Corresponding AuthorWang WX(王文学); Liu LQ(刘连庆)
Affiliation1.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
Recommended Citation
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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