Beamforming has a great potential for joint communication and radar sensing (JCAS), which
is becoming a demanding feature on many emerging platforms, such as unmanned aerial …

In this paper, the joint support recovery of several sparse signals whose supports exhibit
similarities is examined. Each sparse signal is acquired using the same noisy linear …

Several exact recovery criteria (ERC) ensuring that orthogonal matching pursuit (OMP)
identifies the correct support of sparse signals have been developed in the last few years …

We consider the sparse recovery problem of signals with an unknown clustering pattern in
the context of multiple measurement vectors (MMVs) using the compressive sensing (CS) …

The simultaneous orthogonal matching pursuit (SOMP) algorithm aims to find the joint
support of a set of sparse signals acquired under a multiple measurement vector model …

The theory of compressed sensing (CS) indicates that a sparse vector lying in a high
dimensional space can be accurately recovered from only a small set of linear …

The work in this dissertation is focused on two areas within the general discipline of
statistical signal processing. First, several new algorithms are developed and exhaustively …

Simultaneous orthogonal matching pursuit (SOMP) is a classical algorithm for solving
multiple measurement vectors (MMV) problems. In this paper, we analyze the theoretical …

Orthogonal matching pursuit (OMP) algorithm for the multiple measurement vectors (MMV) is
a greedy method to find the sparse matrix with few nonzero rows that represents the …

This paper proposes a novel robust sparsity-based space-time adaptive processing (STAP)
algorithm considering array gain/phase errors for airborne radar. The proposed algorithm …