Volume 12 Issue 5
Oct.  2023
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Article Navigation >  Journal of Radars  > 2023  >  12(5): 1044-1055
GAO Zhiqi, SUN Shuchen, HUANG Pingping, et al. Improved L1/2 threshold iterative high resolution SAR imaging algorithm[J]. Journal of Radars, 2023, 12(5): 1044–1055. doi: 10.12000/JR22243
Citation: GAO Zhiqi, SUN Shuchen, HUANG Pingping, et al. Improved L1/2 threshold iterative high resolution SAR imaging algorithm[J]. Journal of Radars, 2023, 12(5): 1044–1055. doi: 10.12000/JR22243
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Improved L1/2 Threshold Iterative High Resolution SAR Imaging Algorithm

DOI: 10.12000/JR22243
  • 1.

    College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, China

  • 2.

    Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China

Funds:  The National Natural Science Foundation of China (61761037, 62071258), The Natural Science Foundation of Inner Mongolia (2021MS06005, 2020ZD18), Basic Scientific Research Business Cost Project of Colleges Directly under the Inner Mongolia (JY20220147)
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  • Corresponding author: HUANG Pingping, hpp@imut.edu.cn
  • Received Date: 2022-12-28
  • Rev Recd Date: 2023-02-05
    • Available Online: 2023-02-08
  • Publish Date: 2023-02-22
    Abstract
  • An improved Synthetic Aperture Radar (SAR) imaging algorithm is proposed to address the issues of low azimuth resolution and noise interference in the sparse sampling condition. Based on the existing L1/2 regularization theory and iterative threshold algorithm, the gradient operator is modified, which can improve the solution accuracy of the reconstructed image and reduce the load of calculation. Then, under full sampling and under-sampling conditions, the original and improved L1/2 iterative threshold algorithm are combined with the approximate observation model to image SAR echo signals and compare their imaging performance. The experimental findings demonstrate that the improved algorithm improves the azimuth resolution of SAR images and has higher convergence performance.

     

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