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 …

The estimation of the directions of arrival (DoAs) of narrow-band signals impinging on a
linear antenna array is addressed within the Bayesian compressive sensing (BCS) …

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 …

A new framework for the problem of sparse support recovery is proposed, which exploits
statistical information about the unknown sparse signal in the form of its correlation. A key …

In this work, a Bayesian approximate message passing algorithm is proposed for solving the
multiple measurement vector (MMV) problem in compressive sensing, in which a collection …

This paper focuses on direction-of-arrival (DOA) estimation of wideband signals, and a
method named wideband covariance matrix sparse representation (W-CMSR) is proposed …

Energy consumption is an important issue in continuous wireless telemonitoring of
physiological signals. Compressed sensing (CS) is a promising framework to address it, due …

Multiple-input multiple-output (MIMO) radar imaging is a new technique which may solve the
motion compensation problem in inverse synthetic aperture radar (ISAR). However, the …

The problem of estimating the dynamic direction of arrival (DOA) of far-field signals
impinging on a uniform linear array, with mutual coupling effects, is addressed. This paper …

A method named covariance matrix sparse representation (CMSR) is developed to detect
the number and estimate the directions of multiple, simultaneous sources by decomposing …