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Fusion of sparse reconstruction algorithms for multiple measurement vectors

Deepa, KG and Ambat, Sooraj K and Hari, KVS (2016) Fusion of sparse reconstruction algorithms for multiple measurement vectors. In: SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES, 41 (11). pp. 1275-1287.

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Official URL: http://dx.doi.org/10.1007/s12046-016-0552-1

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 .

Item Type: Journal Article
Additional Information: Copy right for this article belongs to the INDIAN ACAD SCIENCES, C V RAMAN AVENUE, SADASHIVANAGAR, P B #8005, BANGALORE 560 080, INDIA
Department/Centre: Division of Electrical Sciences > Electrical Communication Engineering
Depositing User: Id for Latest eprints
Date Deposited: 04 Jan 2017 04:51
Last Modified: 05 Jan 2017 09:24
URI: http://eprints.iisc.ac.in/id/eprint/55712

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