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Airborne Radar Forward-Looking Imaging Algorithm Based on Generative Adversarial Networks
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  • Fangning Li,
  • Di Wu,
  • Daiyin Zhu,
  • Mingwei Shen
Fangning Li
Nanjing University of Aeronautics and Astronautics
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Di Wu
Nanjing University of Aeronautics and Astronautics

Corresponding Author:[email protected]

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Daiyin Zhu
Nanjing University of Aeronautics
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Mingwei Shen
Hohai University
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Radar forward-looking imaging is gaining significance due to its convenience in various applications like battlefield reconnaissance, target surveillance and precision guidance. Although synthetic aperture radar (SAR) techniques are commonly used to achieve high azimuth resolution, they suffer from limitations in forward-looking area due to the poor Doppler resolution and the “left-right” ambiguity problem. In recent years, generative adversarial networks (GANs), a common deep learning approach that produces excellent results in image motion blur removal, has been extensively used. This letter proposes building an end-to-end forward-looking imaging network using GAN to produce high-resolution images, which increases the efficiency and quality of imaging. Compared to conventional forward-looking imaging methods such as the deconvolution-based methods, this algorithm eliminates the design and iterative processes of the observation matrix. Simulated and real radar data verified that this approach offers robust recovery and better performance.
17 Apr 2023Submitted to Electronics Letters
17 Apr 2023Submission Checks Completed
17 Apr 2023Assigned to Editor
09 May 2023Reviewer(s) Assigned
29 May 2023Review(s) Completed, Editorial Evaluation Pending
30 May 2023Editorial Decision: Revise Major
29 Jun 20231st Revision Received
30 Jun 2023Submission Checks Completed
30 Jun 2023Assigned to Editor
30 Jun 2023Review(s) Completed, Editorial Evaluation Pending
04 Jul 2023Editorial Decision: Accept