Concatenate and Boost for Multiple Measurement Vector Problems

TL;DR

This paper introduces CoMBo, a novel algorithm that combines concatenation and boosting techniques to recover sparse signals in MMV problems, achieving the theoretical performance limit and outperforming existing methods.

Contribution

The paper proposes a new concatenate and boost (CoMBo) algorithm that attains the theoretical bound for MMV signal recovery, surpassing existing algorithms.

Findings

CoMBo outperforms all existing methods in simulations.

CoMBo achieves the theoretical bound as the number of measurement vectors increases.

Extensive simulations validate the effectiveness of CoMBo.

Abstract

Multiple measurement vector (MMV) problem addresses the recovery of a set of sparse signal vectors that share common non-zero support, and has emerged an important topics in compressed sensing. Even though the fundamental performance limit of recoverable sparsity level has been formally derived, conventional algorithms still exhibit significant performance gaps from the theoretical bound. The main contribution of this paper is a novel concatenate MMV and boost (CoMBo) algorithm that achieves the theoretical bound. More specifically, the algorithm concatenates MMV to a larger dimensional SMV problem and boosts it by multiplying random orthonormal matrices. Extensive simulation results demonstrate that CoMBo outperforms all existing methods and achieves the theoretical bound as the number of measurement vector increases.