Fusion of Sparse Reconstruction Algorithms for Multiple Measurement Vectors

TL;DR

This paper introduces a fusion framework that combines multiple algorithms for recovering shared sparse signals from multiple measurement vectors, improving reconstruction performance over individual methods.

Contribution

It proposes a novel fusion approach that integrates various algorithms for multiple measurement vector sparse recovery, with conditions for enhanced performance.

Findings

Fusion algorithm outperforms individual algorithms in simulations

Conditions identified for achieving better reconstruction accuracy

Demonstrates robustness across different measurement noise levels

Abstract

We consider the recovery of sparse signals that share a common support from multiple measurement vectors. The performance of several algorithms developed for this task depends on parameters like dimension of the sparse signal, dimension of measurement vector, sparsity level, measurement noise. We propose a fusion framework, where several multiple measurement vector reconstruction algorithms participate and the final signal estimate is obtained by combining the signal estimates of the participating algorithms. We present the conditions for achieving a better reconstruction performance than the participating algorithms. Numerical simulations demonstrate that the proposed fusion algorithm often performs better than the participating algorithms.