Volume 6 Issue 1
Apr.  2017
Turn off MathJax
Article Contents
Article Navigation >  Journal of Radars  > 2017  >  6(1): 25-33
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
shu

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

doi: 10.12000/JR16027
  • 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

More Information
  • 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
  • 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.

     

    • FullText(HTML)
  • loading
    • References(19)
  • [1]
    Cumming I G and Wong F H. Digital Processing of Synthetic Aperture Radar Data:Algorithms and Implementation[M]. Norwood, MA, USA, Artech House, 2004:225-367.
    [2]
    Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4):1289-1306. doi: 10.1109/TIT.2006.871582
    [3]
    Baraniuk R G. Compressive sensing[J]. IEEE Signal Processing Magazine, 2007, 24(4):118-120. doi: 10.1109/MSP.2007.4286571
    [4]
    Candes E J, Romberg J K, and Tao T. Stable signal recovery from incomplete and inaccurate measurements[J]. Communications on Pure and Applied Mathematics, 2006, 59(8):1207-1223. doi: 10.1002/(ISSN)1097-0312
    [5]
    Davenport M A, Duarte M F, Eldar Y C, et al.. Compressed Sensing:Theory and Applications[M]. Cambridge, U.K., Cambridge University Press, 2012:1-55.
    [6]
    吴一戎, 洪文, 张冰尘, 等.稀疏微波成像研究进展[J].雷达学报, 2014, 3(4):383-395. http://radars.ie.ac.cn/CN/abstract/abstract196.shtml

    Wu Yi-rong, Hong Wen, Zhang Bing-chen, et al.. Current developments of sparse microwave imaging[J]. Journal of Radars, 2014, 3(4):383-395. http://radars.ie.ac.cn/CN/abstract/abstract196.shtml
    [7]
    Baraniuk R and Steeghs P. Compressive radar imaging[C]. IEEE Radar Conference, Waltham, MA, USA, 2007: 128-133.
    [8]
    Alonso Mariví Tello, López-Dekker Paco, and Mallorquí Jordi J. A novel strategy for radar imaging based on compressive sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(12):4285-4295. doi: 10.1109/TGRS.2010.2051231
    [9]
    Yang Jungang, Thompson J, Huang Xiaotao, et al.. Segmented reconstruction for compressed sensing SAR imaging[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(7):4214-4225. doi: 10.1109/TGRS.2012.2227060
    [10]
    Fang Jian, Xu Zongben, Zhang Bingchen, et al.. Fast compressed sensing SAR imaging based on approximated observation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(1):352-363. doi: 10.1109/JSTARS.2013.2263309
    [11]
    Dong Xiao and Zhang Yunhua. A novel compressive sensing algorithm for SAR imaging[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(2):708-720. doi: 10.1109/JSTARS.2013.2291578
    [12]
    Bu Hongxia, Tao Ran, Bai Xia, et al.. A novel SAR imaging algorithm based on compressed sensing[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(5):1003-1007. doi: 10.1109/LGRS.2014.2372319
    [13]
    Yang Dong, Liao Guisheng, Zhu Shengqi, et al.. SAR imaging with undersampled data via matrix completion[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(9):1539-1543. doi: 10.1109/LGRS.2014.2300170
    [14]
    Dong Xiao and Zhang Yunhua. A MAP approach for 1-bit compressive sensing in synthetic aperture radar imaging[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(6):1237-1241. doi: 10.1109/LGRS.2015.2390623
    [15]
    Zhang Siqian, Zhu Yutao, Dong Ganggang, et al.. Truncated SVD-based compressive sensing for downward-looking three-dimensional SAR imaging with uniform nonuniform linear array[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(9):1853-1857. doi: 10.1109/LGRS.2015.2431254
    [16]
    Bi Hui, Jiang Chenglong, Zhang Bingchen, et al.. Radar change imaging with undersampled data based on matrix completion and Bayesian compressive sensing[J].IEEE Geoscience and Remote Sensing Letters, 2015, 12(7):1546-1550. doi: 10.1109/LGRS.2015.2412677
    [17]
    Khwaja Ahmed Shaharyar, Ferro-Famil Laurent, and Pottier Eric. Efficient SAR raw data generation for anisotropic urban scenes based on inverse processing[J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(4):757-761. doi: 10.1109/LGRS.2009.2024559
    [18]
    Blumensath T and Davies M E. Iterative hard thresholding for compressed sensing[J]. Applied and Computational Harmonic Analysis, 2009, 27(3):265-274. doi: 10.1016/j.acha.2009.04.002
    [19]
    Daubechies Ingrid, Defrise Michel, and De Mol Christine. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint[J]. Communications on Pure and Applied Mathematics, 2004, 57(11):1413-1457. doi: 10.1002/(ISSN)1097-0312
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML
    Article views(3267) PDF downloads(880) Cited by()
    Proportional views
    Related

    京ICP备20021838号-8   Supported by: Beijing Renhe Information Technology Co. Ltd     百度统计

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return