Volume 12 Issue 6
Dec.  2023
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Article Navigation >  Journal of Radars  > 2023  >  12(6): 1166-1178
REN Lingyun, WU Di, ZHU Daiyin, et al. Forward-looking imaging via iterative super-resolution estimation in airborne multi-channel radar[J]. Journal of Radars, 2023, 12(6): 1166–1178. doi: 10.12000/JR23085
Citation: REN Lingyun, WU Di, ZHU Daiyin, et al. Forward-looking imaging via iterative super-resolution estimation in airborne multi-channel radar[J]. Journal of Radars, 2023, 12(6): 1166–1178. doi: 10.12000/JR23085
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Forward-looking Imaging via Iterative Super-resolution Estimation in Airborne Multi-channel Radar

DOI: 10.12000/JR23085 CSTR: 32380.14.JR23085

  • College of Electronic Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Funds:  The National Natural Science Foundation of China (62271252)
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  • Corresponding author: WU Di, wudi82@nuaa.edu.cn; ZHU Daiyin, zhudy@nuaa.edu.cn
  • Received Date: 2023-05-10
  • Rev Recd Date: 2023-07-10
    • Available Online: 2023-07-14
  • Publish Date: 2023-07-27
    Abstract
  • The direction of arrival estimation algorithm can overcome the Rayleigh limit, effectively separate multiple targets within the main lobe, and improve the azimuth resolution when applied to forward-looking imaging in airborne multi-channel radar. However, the limited antenna beam coverage and rapid scanning result in a scarcity of data samples available for covariance matrix estimation, leading to direction and amplitude estimation errors. Herein, we propose a forward-looking imaging algorithm based on single-snapshot iterative super-resolution estimation. The algorithm performs single-snapshot iterative spectral estimation to accurately determine the azimuth and amplitude of the target. Subsequently, a high-resolution image is achieved through non-coherent accumulation. Simulation and experimental data processing results show that the proposed algorithm can resolve multiple targets, significantly improving the azimuth resolution of the forward-looking image compared with traditional forward-looking imaging algorithms. Moreover, it ensures the accurate reconstruction of point targets and contour reconstruction of area targets.

     

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