Volume 13 Issue 5
Sep.  2024
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Article Navigation >  Journal of Radars  > 2024  >  13(5): 1073-1091
MAO Deqing, YANG Jianyu, YANG Mingjie, et al. IAA-Net: An iterative adaptive approach for angular super-resolution imaging of real aperture scanning radar[J]. Journal of Radars, 2024, 13(5): 1073–1091. doi: 10.12000/JR24063
Citation: MAO Deqing, YANG Jianyu, YANG Mingjie, et al. IAA-Net: An iterative adaptive approach for angular super-resolution imaging of real aperture scanning radar[J]. Journal of Radars, 2024, 13(5): 1073–1091. doi: 10.12000/JR24063
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IAA-Net: An Iterative Adaptive Approach for Angular Super-resolution Imaging of Real Aperture Scanning Radar

DOI: 10.12000/JR24063 CSTR: 32380.14.JR24063

  • School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds:  The National Natural Science Foundation of China (62301131), The China Postdoctoral Science Foundation (BX20220055, 2022M720667)
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    Abstract
  • Real Aperture Radar (RAR) observes wide-scope target information by scanning its antenna. However, because of the limited antenna size, the angular resolution of RAR is much lower than the range resolution. Angular super-resolution methods can be applied to enhance the angular resolution of RAR by inverting the low-rank steering matrix based on the convolution relationship between the antenna pattern and target scatterings. Because of the low-rank characteristics of the antenna steering matrix, traditional angular super-resolution methods suffer from manual parameter selection and high computational complexity. In particular, these methods exhibit poor super-resolution angular resolution at low signal-to-noise ratios. To address these problems, an iterative adaptive approach for angular super-resolution imaging of scanning RAR is proposed by combining the traditional Iterative Adaptive Approach (IAA) with a deep network framework, namely IAA-Net. First, the angular super-resolution problem for RAR is transformed into an echo autocorrelation matrix inversion problem to mitigate the ill-posed condition of the inverse matrix. Second, a learnable repairing matrix is introduced into the IAA procedure to combine the IAA algorithm with the deep network framework. Finally, the echo autocorrelation matrix is updated via iterative learning to improve the angular resolution. Simulation and experimental results demonstrate that the proposed method avoids manual parameter selection and reduces computational complexity. The proposed method provides high angular resolution under a low signal-to-noise ratio because of the learning ability of the deep network.

     

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