Several problems arising in electromagnetics can be directly formulated or suitably recast for
an effective solution within the compressive sensing (CS) framework. This has motivated a …

We address the sparse signal recovery problem in the context of multiple measurement
vectors (MMV) when elements in each nonzero row of the solution matrix are temporally …

Modern radar systems tend to utilize high bandwidth, which requires high sampling rate, and
in many cases, these systems involve phased array configurations with a large number of …

A set of vectors is called jointly sparse when its elements share a common sparsity pattern.
We demonstrate how the direction-of-arrival (DOA) estimation problem can be cast as the …

In the area of sparse recovery, numerous researches hint that non-convex penalties might
induce better sparsity than convex ones, but up until now those corresponding non-convex …

In this paper, we develop a framework to design sensing matrices for compressive sensing
applications that lead to good mean squared error (MSE) performance subject to sensing …

In recent years, most of the studies have shown that the generalized iterated shrinkage
thresholdings (GISTs) have become the commonly used first-order optimization algorithms …

For downward-looking linear array 3-D synthetic aperture radar (SAR), the azimuth and
cross-track resolution are unacceptable due to the length limitation of synthetic aperture and …

The sparse recovery methods utilize the ℓ p-norm-based regularization in the estimation
problem with 0≤ p≤ 1. These methods have a better utility when the number of …

This paper proposes vector minimax concave (VMC) penalty for sparse representation using
tools of Moreau envelope. The VMC penalty is a weighted MC function; by fine tuning the …