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LI Weixin, LI Ming, CHEN Hongmeng, et al. Fast radar forward-looking super-resolution imaging for abnormal echo data[J]. Journal of Radars, in press. doi: 10.12000/JR23209
Citation: LI Weixin, LI Ming, CHEN Hongmeng, et al. Fast radar forward-looking super-resolution imaging for abnormal echo data[J]. Journal of Radars, in press. doi: 10.12000/JR23209
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Fast Radar Forward-looking Super-resolution Imaging for Abnormal Echo Data

doi: 10.12000/JR23209
  • 1.

    School of Information Science and Engineering, University of Jinan, Jinan 250022, China

  • 2.

    National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

  • 3.

    Beijing Institute of Radio Measurement, Beijing 100854, China

Funds:  Innovation Capability Enhancement Program for Small and Medium-sized Technological Enterprises of Shandong Province (2023TSGC0332, 2023TSGC0141)
More Information
  • Corresponding author: XIN Dongjin, ise_xindj@ujn.edu.cn
  • Received Date: 2023-10-29
  • Rev Recd Date: 2024-01-12
    • Available Online: 2024-01-19
    Abstract
  • Forward-looking imaging of airborne scanning radar is widely used in situation awareness, autonomous navigation and terrain following. When the radar is influenced by unintentional temporally sporadic electromagnetic interference or abnormal equipment performance, the echo signal contains outliers. Existing super-resolution methods can suppress outliers and improve azimuth resolution, but the real-time computing problem is not considered. In this study, we propose an airborne scanning radar super-resolution method to achieve fast forward-looking imaging when echo data are abnormal. First, we propose using the Student-t distribution to model noise. Then, the expectation-maximization method is used to estimate the parameters. Inspired by the truncated singular value decomposition method, we introduce the truncated unitary matrix into the estimation formula of the target scattering coefficient. Finally, the size of inverse matrix is reduced and the computational complexity of parameter estimation is reduced through matrix transformation. The simulation results show that the proposed method can improve the azimuth resolution of forward-looking imaging in a shorter time, and suppress outliers in echo data.

     

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