Volume 10 Issue 5
Oct.  2021
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Article Navigation >  Journal of Radars  > 2021  >  10(5): 718-724
JIANG Yanwen, FAN Hongqi, and LI Shuangxun. A sparse Bayesian learning approach for vortex electromagnetic wave three-dimensional imaging in the Terahertz band[J]. Journal of Radars, 2021, 10(5): 718–724. doi: 10.12000/JR21151
Citation: JIANG Yanwen, FAN Hongqi, and LI Shuangxun. A sparse Bayesian learning approach for vortex electromagnetic wave three-dimensional imaging in the Terahertz band[J]. Journal of Radars, 2021, 10(5): 718–724. doi: 10.12000/JR21151
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A Sparse Bayesian Learning Approach for Vortex Electromagnetic Wave Three-dimensional Imaging in the Terahertz Band

doi: 10.12000/JR21151

  • College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Funds:  The National Natural Science Foundation of China (61871386, 62171446)
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  • Corresponding author: JIANG Yanwen, j1991yuwen@163.com
  • Received Date: 2021-10-16
  • Rev Recd Date: 2021-10-25
    • Available Online: 2021-10-26
  • Publish Date: 2021-10-28
    Abstract
  • In the Inverse Synthetic Aperture Radar (ISAR) imaging system, when the terahertz radar transmits the wide bandwidth signal and vortex electromagnetic wave, Three-Dimensional (3D) high-resolution imaging can be achieved through information decoupling based on the differential radiation field formed by the vortex electromagnetic wave and the synthetic aperture formed by the relative movement of the radar and the target. Accordingly, a 3D imaging model based on the terahertz vortex electromagnetic wave ISAR is established. A new image reconstruction method is proposed based on the Sparse Bayesian Learning (SBL) and subregion amplitude threshold setting methods. The proposed method can significantly simplify the imaging procedure and reduce the computational load. The simulation results indicate that the proposed SBL method can achieve a higher resolution than the conventional fast Fourier transform-based method, and its reconstruction performance increases with an increase in the signal-to-noise ratio.

     

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    • References(19)
  • [1]
    LIU Kang, LIU Hongyan, QIN Yuliang, et al. Generation of OAM beams using phased array in the microwave band[J]. IEEE Transactions on Antennas and Propagation, 2016, 64(9): 3850–3857. doi: 10.1109/TAP.2016.2589960
    [2]
    郭忠义, 汪彦哲, 郑群, 等. 涡旋电磁波天线技术研究进展[J]. 雷达学报, 2019, 8(5): 631–655. doi: 10.12000/JR19091

    GUO Zhongyi, WANG Yanzhe, ZHENG Qun, et al. Advances of research on antenna technology of vortex electromagnetic waves[J]. Journal of Radars, 2019, 8(5): 631–655. doi: 10.12000/JR19091
    [3]
    SHEN Fei, MU Jiangnan, GUO Kai, et al. Generating circularly polarized vortex electromagnetic waves by the conical conformal patch antenna[J]. IEEE Transactions on Antennas and Propagation, 2019, 67(9): 5763–5771. doi: 10.1109/TAP.2019.2922545
    [4]
    郭桂蓉, 胡卫东, 杜小勇. 基于电磁涡旋的雷达目标成像[J]. 国防科技大学学报, 2013, 35(6): 71–76. doi: 10.3969/j.issn.1001-2486.2013.06.013

    GUO Guirong, HU Weidong, and DU Xiaoyong. Electromagnetic vortex based radar target imaging[J]. Journal of National University of Defense Technology, 2013, 35(6): 71–76. doi: 10.3969/j.issn.1001-2486.2013.06.013
    [5]
    BU Xiangxi, ZHANG Zhuo, CHEN Longyong, et al. Implementation of vortex electromagnetic waves High-Resolution synthetic aperture radar imaging[J]. IEEE Antennas and Wireless Propagation Letters, 2018, 17(5): 764–767. doi: 10.1109/LAWP.2018.2814980
    [6]
    LIU Kang, LIU Hongyan, SHA W E I, et al. Backward scattering of electrically large standard objects illuminated by OAM beams[J]. IEEE Antennas and Wireless Propagation Letters, 2020, 19(7): 1167–1171. doi: 10.1109/LAWP.2020.2993687
    [7]
    LIU Kang, CHENG Yongqiang, YANG Zhaocheng, et al. Orbital-angular-momentum-based electromagnetic vortex imaging[J]. IEEE Antennas and Wireless Propagation Letters, 2015, 14: 711–714. doi: 10.1109/LAWP.2014.2376970
    [8]
    YANG Taoli, LI Shihua, XU Ou, et al. Three dimensional SAR imaging based on vortex electromagnetic waves[J]. Remote Sensing Letters, 2018, 9(4): 343–352. doi: 10.1080/2150704X.2017.1421791
    [9]
    JIANG Yanwen, LIU Kang, WANG Hongqiang, et al. Orbital-angular-momentum-based ISAR imaging at terahertz frequencies[J]. IEEE Sensors Journal, 2018, 18(22): 9230–9235. doi: 10.1109/JSEN.2018.2869047
    [10]
    保铮, 邢孟道, 王彤. 雷达成像技术[M]. 北京: 电子工业出版社, 2005.

    BAO Zheng, XING Mengdao, and WANG Tong. Technology of Radar Imaging[M]. Beijing: Publishing House of Electronics Industry, 2005.
    [11]
    GUI Shuliang, LI Jin, and PI Yiming. Security imaging for multi-target screening based on adaptive scene segmentation with terahertz radar[J]. IEEE Sensors Journal, 2019, 19(7): 2675–2684. doi: 10.1109/JSEN.2018.2889884
    [12]
    JIANG Yanwen, WANG Hongqiang, QIN Yuliang, et al. A three-dimensional surface imaging method using THz dual-frequency interferometry[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(11): 1651–1655. doi: 10.1109/LGRS.2016.2600570
    [13]
    SHEEN D M, MCMAKIN D L, and HALL T E. Three-dimensional millimeter-wave imaging for concealed weapon detection[J]. IEEE Transactions on Microwave Theory and Techniques, 2001, 49(9): 1581–1592. doi: 10.1109/22.942570
    [14]
    CARIS M, STANKO S, PALM S, et al. 300 GHz radar for high resolution SAR and ISAR applications[C]. 2015 16th International Radar Symposium, Dresden, Germany, 2015: 577–580. doi: 10.1109/IRS.2015.7226313.
    [15]
    WIPF D P and RAO B D. Sparse Bayesian learning for basis selection[J]. IEEE Transactions on Signal Processing, 2004, 52(8): 2153–2164. doi: 10.1109/TSP.2004.831016
    [16]
    DESAI M D and JENKINS W K. Convolution backprojection image reconstruction for spotlight mode synthetic aperture radar[J]. IEEE Transactions on Image Processing, 1992, 1(4): 505–517. doi: 10.1109/83.199920
    [17]
    蒋彦雯. 太赫兹阵列雷达三维成像技术研究[D]. [博士论文], 国防科技大学, 2018.

    JIANG Yanwen. Study on the 3D imaging technology for terahertz array radar[D]. [Ph. D. dissertation], National University of Defense Technology, 2018.
    [18]
    SEEGER M W and WIPF D P. Variational Bayesian inference techniques[J]. IEEE Signal Processing Magazine, 2010, 27(6): 81–91. doi: 10.1109/MSP.2010.938082
    [19]
    GONZALEZ R C, WOODS R E, EDDINS S L. 阮秋琦, 阮宇智, 译. 数字图像处理[M]. 3版. 北京: 电子工业出版社, 2020.

    GONZALEZ R C, WOODS R E, EDDINS S L. RUAN Qiuqi, RUAN Yuzhi, translation. Digital Image Processing[M]. 3rd ed. Beijing: Publishing House of Electronics Industry, 2020.
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