High‐resolution radar imaging requires wide bandwidths and highly dense samplings of
both time/frequency and spatial domains. However, the rapidly developing field of …

The theory of compressed sampling (CS) indicates that exact recovery of an unknown
sparse signal can be achieved from very limited samples. For inversed synthetic aperture …

The applicability of compressive sensing (CS) to radar imaging has been recently proven
and its capability to construct reliable radar images from a limited set of measurements …

From the theory of compressive sensing (CS), we know that the exact recovery of an
unknown sparse signal can be achieved from limited measurements by solving a sparsity …

To achieve high-resolution two dimension images, synthetic aperture radar (SAR) with ultra
wide-band faces considerably technical challenges such as long data collection time, huge …

In actual applications of inverse synthetic aperture radar (ISAR), continuous measurements
may be impossible or the collection of data during some periods are not valid in a long …

Based on the observation that sparsity assumption is well satisfied in the synthetic aperture
radar (SAR) imaging applications, there is increasing interest in utilizing compressive …

Compressive sensing (CS) enables imaging with less data, assuming sparsity in certain
domains. We proposed a groundbreaking coarse-to-fine (CTF) estimation method that …

Millimeter-wave (MMW) ground based (GB) synthetic aperture radar (SAR) and inverse SAR
(ISAR) imaging are the powerful tools for the detection of foreign object debris (FOD) and …

This paper presents an innovative Compressive Sensing (CS) based algorithm for synthetic
aperture radar (SAR) imaging, aimed at achieving high-resolution imaging. Traditional …