# Fusion of Sparse Reconstruction Algorithms for Multiple Measurement Vectors

Source: [Fusion of Sparse Reconstruction Algorithms for Multiple Measurement Vectors](<https://www.sota2.com/research/paper/68fa51ba1e209131ce975e1e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T11:33:07.240Z

Authors: Deepa K. G., Sooraj K. Ambat, K.V.S. Hari

Published: 2015-04-06T18:26:25.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1504.01705>)

[PDF](<https://arxiv.org/pdf/1504.01705.pdf>)