We address the problem of high-resolution radar imaging in complex environments in a
Bayesian framework. We perform model order selection and sparse weights estimation via …

We address the problem of high-resolution radar imaging in low signal-to-noise ratio (SNR)
environments in an approximate Bayesian inference framework. First, the probabilistic …

In high-resolution radar imaging, the time-varying Doppler induced by maneuvering targets
generally invalidates traditional methods. Although sparse signal reconstruction methods …

The coherent processing and superresolution of multi-band radar data from multiple
spatially collocated radars is addressed by utilizing a sparse representation technique in this …

Conventional space-time adaptive processing suffers from the requirement of a large
number of secondary samples. In this paper, a novel method is proposed to accurately …

Although several works have been done on high-resolution inverse synthetic aperture radar
(ISAR) imaging via compressive sampling technology, they only explore sparse priors of …

Radar coincidence imaging (RCI) is a high‐resolution staring imaging technique motivated
by classical optical coincidence imaging. In RCI, sparse reconstruction methods are …

Azimuth super-resolution is an efficient method to enhance the angular resolution of
scanning radar in forward-looking area. The existing super-resolution methods are limited in …

This paper addresses the problem of wall clutter mitigation and image reconstruction for
through-wall radar imaging (TWRI) of stationary targets by seeking a model that incorporates …

This paper presents a novel inverse synthetic aperture radar (ISAR) imaging method by
exploiting the inherent continuity of the scatterers on the target scene to obtain enhanced …