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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