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\begin{document}

\title{Airborne Radar Forward-Looking Imaging Algorithm Based on Generative
Adversarial Networks}



\author[1]{Fangning Li}%
\author[1]{Di Wu}%
\author[2]{Daiyin Zhu}%
\author[3]{Mingwei Shen}%
\affil[1]{Nanjing University of Aeronautics and Astronautics}%
\affil[2]{Nanjing University of Aeronautics }%
\affil[3]{Hohai University}%


\vspace{-1em}



  
  \date{June 17, 2024}


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\begin{abstract}
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.%
\end{abstract}%



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