The exploitation of sparsity has significantly advanced the field of radar imaging over the last
few decades, leading to substantial improvements in the resolution and quality of the …

Inverse synthetic aperture radar (ISAR) imaging for the sparse aperture data is affected by
considerable artifacts, because under-sampling of data produces high-level grating and side …

Airborne synthetic aperture radar (SAR) systems often encounter the threats of interceptors
or electronic countermeasures (ECM) and suffer from motion measurement errors. In order …

Most research on sparsity-driven synthetic aperture radar (SAR) imaging has been carried
out in-norm regularization and considers that the SAR image contains only targets and …

In this paper, we study the problem of interrupted synthetic aperture radar (SAR) imaging
and coherent change detection (CCD) in the setting of gapped collections with missing …

The recent success of deep learning has lead to growing interest in applying these methods
to signal processing problems. This paper explores the applications of deep learning to …

For sparse aperture (SA) radar imaging, the phase errors are difficult to be estimated, which
challenges the traditional autofocusing for inverse synthetic aperture radar (ISAR) imaging …

Sparsity-regularized synthetic aperture radar (SAR) imaging framework has shown its
remarkable performance to generate a feature-enhanced high-resolution image, in which a …

Autofocus is an essential technique for airborne synthetic aperture radar (SAR) imaging to
correct phase errors mainly due to unexpected motion error. There are several well-known …

The radar autofocus problem arises in situations where radar measurements are acquired of
a scene using antennas that suffer from position ambiguity. Current techniques model the …