2D normalized iterative hard thresholding algorithm for fast compressive radar imaging
文献类型：期刊论文
| 作者 | Yang WG(杨文广); Yang J(杨佳); Li GX(李恭新); Liu LQ(刘连庆) ; Wang WX(王文学); Wang YC(王越超)
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| 刊名 | Remote Sensing
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| 出版日期 | 2017 |
| 卷号 | 9期号:6页码:1-16 |
| 关键词 | fast compressive radar imaging compressive sensing two dimensional normalized iterative hard thresholding (2D-NIHT) algorithm compressive radar imaging model reconstruction performance |
| ISSN号 | 2072-4292 |
| 产权排序 | 1 |
| 通讯作者 | Wang WX(王文学) ; Liu LQ(刘连庆) |
| 中文摘要 | 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. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Remote Sensing |
| 研究领域[WOS] | Remote Sensing |
| 关键词[WOS] | SPARSE DECOMPOSITION ; SIGNAL RECOVERY ; MATRICES |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000404623900110 |
| 源URL | [http://ir.sia.cn/handle/173321/20759]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 作者单位 | 1.University of the Chinese Academy of Sciences, Beijing 100049, China 2.State Key Laboratory of Robotics, Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang 110016, China |
| 推荐引用方式 GB/T 7714 | Yang WG,Yang J,Li GX,et al. 2D normalized iterative hard thresholding algorithm for fast compressive radar imaging[J]. Remote Sensing,2017,9(6):1-16. |
| APA | Yang WG,Yang J,Li GX,Liu LQ,Wang WX,&Wang YC.(2017).2D normalized iterative hard thresholding algorithm for fast compressive radar imaging.Remote Sensing,9(6),1-16. |
| MLA | Yang WG,et al."2D normalized iterative hard thresholding algorithm for fast compressive radar imaging".Remote Sensing 9.6(2017):1-16. |
入库方式： OAI收割
来源：沈阳自动化研究所
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