CS-SAR Imaging Method Based on Inverse Omega-K Algorithm

Hu Jingqiu; Liu Falin; Zhou Chongbin; Li Bo; Wang Dongjin

doi:10.12000/JR16027

Volume 6 Issue 1

Apr. 2017

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Abstract

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Hu Jingqiu, Liu Falin, Zhou Chongbin, Li Bo, Wang Dongjin. CS-SAR Imaging Method Based on Inverse Omega-K Algorithm[J]. Journal of Radars, 2017, 6(1): 25-33. doi: 10.12000/JR16027

Citation:

Hu Jingqiu, Liu Falin, Zhou Chongbin, Li Bo, Wang Dongjin. CS-SAR Imaging Method Based on Inverse Omega-K Algorithm[J]. Journal of Radars, 2017, 6(1): 25-33. doi: 10.12000/JR16027

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CS-SAR Imaging Method Based on Inverse Omega-K Algorithm

DOI: 10.12000/JR16027

Hu Jingqiu^①,②,
Liu Falin^{①,②
,
,},
Zhou Chongbin^①,②,
Li Bo^①,②,
Wang Dongjin^①,②

①.
Department of EEIS, University of Science and Technology of China, Hefei 230027, China
②.
Key Laboratory of Electromagnetic Space Information, Chinese Academy of Sciences, Hefei 230027, China

Funds:

The National Natural Science Foundation of China 61431016

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Author Bio:
Hu Jingqiu (1992-) was born in Anhui, China. She is currently a master student in University of Science and Technology of China. She received her Bachelor degree in electronic engineering and information science from University of Science and Technology of China. Her current research interests include SAR imaging, compressed sensing, and sparse signal reconstruction

Liu Falin (1963-) was born in Xingtai, Hebei. He received the B.E. degree from Tsinghua University, Beijing, China, in 1985, and the M.E. and Ph.D. degrees both from University of Science and Technology of China (USTC), Hefei, China, in 1988 and 2004, respectively. Since 1988, he has been with the Department of Electronic Engineering and Information Science, USTC, where he is now a professor. His research interests include mm-wave devices, computational electromagnetics, microwave communications, and radar imaging. E-mail:liufl@ustc.edu.cn

Zhou Chongbin (1988-) was born in Luoyang, Henan Province, China. He received the B.Eng. degree from University of Science and Technology of China (USTC), Hefei, China, in 2011. He is currently working toward the Ph.D. degree at USTC. His research interests include compressive sensing, signal processing, and radar imaging

Li Bo (1991-) was born in Baoji, Shaanxi Province, China. He received the Bachelor degree from Xidian University, Xi'an, China, in 2013. He is currently working toward the Ph.D. degree at USTC. His research interests include compressive sensing, signal processing, and radar imaging

Wang Dongjin (1955-) was born in Huainan, Anhui Province. He received the Bachelor's degree from University of Science and Technology of China (USTC), Hefei, China, in 1982, and the M.E. degree from Nanjing Institute of Electronic Technology, Nanjing, China, in 1985. He has been a full professor since 1998. Prof. Wang had been the vice president of USTC since 2003 and is now the director of the Key Laboratory of Electromagnetic Space Information, Chinese Academy of Sciences. His research interests include electromagnetic theory, mm-wave communications and radar systems, and applications
Corresponding author: Liu Falin. E-mail:liufl@ustc.edu.cn
Received Date: 2016-01-30
Rev Recd Date: 2016-03-29

Available Online: 2016-06-03

Publish Date: 2017-02-28

Abstract

Abstract

Compressed Sensing (CS) has been proved to be effective in Synthetic Aperture Radar (SAR) imaging. Previous CS-SAR imaging algorithms are very time consuming, especially for producing high-resolution images. In this study, we propose a new CS-SAR imaging method based on the well-known omega-K algorithm, which is precise and convenient to use in SAR imaging. First, we derive an inverse omega-K algorithm to directly obtain echoes without any convolution between the transmitted signal and scene. Then, we formulate the SAR imaging problem into a sparse regularization problem and solve it using an iterative thresholding algorithm. With our derived inverse omega-K algorithm, we can save significant amounts of computation time and computer memory usage. Simulation results show that the proposed method can effectively recover SAR images with much less data than that required by the Nyquist rate.
- Synthetic Aperture Radar (SAR),
- Compressed Sensing (CS),
- Omega-K Algorithm (OKA),
- Iterative thresholding algorithm

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References(19)

References

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