Compressive sensing generally relies on the ℓ 2 norm for data fidelity, whereas in many
applications, robust estimators are needed. Among the scenarios in which robust …

Massive antenna arrays can be used to meet the requirements of 5G, by exploiting different
spatial signatures of users. This same property can also be harnessed to determine the …

A generalized noise reconstruction approach is proposed for improved direction-of-arrival
(DOA) estimation. First, coarse estimation of the noise component in the received data is …

This letter investigates the problem of sparse signal recovery in the presence of additive
impulsive noise. The heavytailed impulsive noise is well modeled with stable distributions …

In this paper, we introduce a proximal-proximal majorization-minimization (PPMM) algorithm
for nonconvex rank regression problems. The basic idea of the algorithm is to apply the …

In the problem of image inpainting, one popular approach is based on low-rank matrix
completion. Compared with other methods which need to convert the image into vectors or …

Sparse solutions of linear systems of equations are essential in many applications of seismic
data processing. These systems arise in many denoising algorithms, such as those that use …

For a moving target indicating (MTI) radar system to eliminate “blind speeds” or synthetic
aperture radar (SAR) to eliminate “blind ranges,” two or more pulse repetition intervals …

We propose a robust sparse recovery formulation in impulsive noise, where ℓ 1 norm as the
metric for the residual error and a class of weakly convex functions for inducing sparsity are …

Robust sparse signal recovery against impulsive noise is a core issue in many applications.
Numerous methods have been proposed to recover the sparse signal from measurements …