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FAN Xiaoyi, YANG Xiaocheng, WU Lin, YAN Jingye, XU Lu. Imaging Method of Synthetic Aperture Radio Telescope Based on Minimax Concave Penalty (in Chinese). Chinese Journal of Space Science, 2025, 45(6): 1597-1606 doi: 10.11728/cjss2025.06.2024-0186
Citation: FAN Xiaoyi, YANG Xiaocheng, WU Lin, YAN Jingye, XU Lu. Imaging Method of Synthetic Aperture Radio Telescope Based on Minimax Concave Penalty (in Chinese). Chinese Journal of Space Science, 2025, 45(6): 1597-1606 doi: 10.11728/cjss2025.06.2024-0186
shu

Imaging Method of Synthetic Aperture Radio Telescope Based on Minimax Concave Penalty

doi: 10.11728/cjss2025.06.2024-0186 cstr: 32142.14.cjss.2024-0186
  • 1.

    School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018

  • 2.

    State Key Laboratory of Solar Activity and Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing 100190

  • Received Date: 2024-12-16
  • Rev Recd Date: 2025-03-26
    • Available Online: 2025-03-27
    Abstract
  • In the synthetic aperture radio telescope, the reconstruction of an image from the measured visibility function is an ill-posed inverse problem on account of limitations in the visibility sampling scheme. Although compressed sensing technology has been successfully applied in interferometric imaging of a synthetic aperture radio telescope, the traditional compressed sensing algorithms make use of L1 norm minimization to approximately replace L0 norm minimization, which brings a certain deviation. To address this issue, a new imaging method of a synthetic aperture radio telescope based on a minimax concave penalty is proposed in this paper. This method approximates the L0 norm by the minimax concave penalty, and efficiently solves the non-convex minimization model by the proximal gradient algorithm. Compared with the L1 norm, the minimax concave penalty is a closer approximation of the L0 norm. In the iterative process, the maximum likelihood estimation is employed to adaptively select the regularization parameter, thereby enhancing the accuracy of the reconstruction results. In addition, the restart and adaptive strategies are adopted to avoid the oscillations during the iteration process and improve the convergence speed of the algorithm. Based on a simulated general array using a random sampling pattern of variable density and the Square Kilometer Array, numerical simulation experiments have been conducted to compare the performance of the proposed method in terms of reconstruction quality and computation speed with respect to classical imaging methods such as the Sparsity Averaging Reweighted Analysis (SARA) and Artificial Intelligence for Regularization in radio-interferometric Imaging (AIRI). The numerical experiment results indicate that compared with the SARA and AIRI approaches, the proposed approach can effectively reduce the reconstruction error and improve the reconstruction quality. The computation speed of the proposed approach is markedly faster than the SARA and slightly slower than the AIRI. Furthermore, the proposed approach exhibits superior robustness to noise interference than the SARA and AIRI approaches.

     

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