  In this paper, we introduce an innovative super resolution approach to
emerging modes of near-field synthetic aperture radar (SAR) imaging. Recent
research extends convolutional neural network (CNN) architectures from the
optical to the electromagnetic domain to achieve super resolution on images
generated from radar signaling. Specifically, near-field synthetic aperture
radar (SAR) imaging, a method for generating high-resolution images by scanning
a radar across space to create a synthetic aperture, is of interest due to its
high-fidelity spatial sensing capability, low cost devices, and large
application space. Since SAR imaging requires large aperture sizes to achieve
high resolution, super-resolution algorithms are valuable for many
applications. Freehand smartphone SAR, an emerging sensing modality, requires
irregular SAR apertures in the near-field and computation on mobile devices.
Achieving efficient high-resolution SAR images from irregularly sampled data
collected by freehand motion of a smartphone is a challenging task. In this
paper, we propose a novel CNN architecture to achieve SAR image
super-resolution for mobile applications by employing state-of-the-art SAR
processing and deep learning techniques. The proposed algorithm is verified via
simulation and an empirical study. Our algorithm demonstrates high-efficiency
and high-resolution radar imaging for near-field scenarios with irregular
scanning geometries.
