A Robust Algorithm for Joint-Sparse Recovery

Abstract

We address the problem of finding a set of sparse signals that have nonzero coefficients in the same locations from a set of their compressed measurements. A mixed 2 0 norm optimization approach is considered. A cost function appropriate to the joint-sparse problem is developed, and an algorithm is derived. Compared to other convex relaxation based techniques, the results obtained by the proposed method show a clear improvement in both noiseless and noisy environments.

Joint-sparse recovery in distributed networks via cooperative support fusion

“…When all measurement vectors and measurement matrices are centrally available at a FC, many centralized algorithms [4,5,7] can be used to solve the joint-sparse recovery problem at the FC. However, these centralized algorithms are not suitable for distributed networks where each sensor has limited computational and communication resources.…”

“…Literatures of joint-sparse recovery [3,4,5] reveal that exploiting such structure can effectively improve the recovery performance, compared with traditional compressed sensing (CS) [6] with signal measurement vector (SMV). However, most of the existing works focus on centralized scenario where all measurement vectors are centrally available at a central fusion center (FC) and many algorithms have been developed to solve this centralized jointsparse recovery problem, such as mix-norm minimization algorithm [4,5], ReMBo algorithm [7], etc.…”

“…However, most of the existing works focus on centralized scenario where all measurement vectors are centrally available at a central fusion center (FC) and many algorithms have been developed to solve this centralized jointsparse recovery problem, such as mix-norm minimization algorithm [4,5], ReMBo algorithm [7], etc.…”

A Sparse SAR Imaging Method Based on Multiple Measurement Vectors Model

“…MMV problems derive from many applications areas, such as magnetoencephalography, which is a modality for imaging the brain [13]. Similar conceptions were also developed in the context of array processing [14,15] equalization of sparse communication channels [16,17], and more recently line spectrum denoising [18] and cognitive radio communications [19]. In this paper, we want to incorporate this fast growing field into SAR imaging applications.…”

Robustness of orthogonal matching pursuit for multiple measurement vectors in noisy scenario

“…Other algorithms, including FOCal Underdetermined System Solver (FOCUSS) [5] and Joint 2,0 Approximation Algorithm (JLZA) [9], are also put forward. When Y and Φ are unperturbed, the recovery process of OMPMMV can be written as:…”