Modified Greedy Pursuits for Improving Sparse Recovery

Deepa, KG and Ambat, Sooraj K and Hari, KVS (2014) Modified Greedy Pursuits for Improving Sparse Recovery. In: 2014 TWENTIETH NATIONAL CONFERENCE ON COMMUNICATIONS (NCC), FEB 28-MAR 02, 2014, Kanpur, INDIA.

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Abstract

Compressive Sensing (CS) theory combines the signal sampling and compression for sparse signals resulting in reduction in sampling rate. In recent years, many recovery algorithms have been proposed to reconstruct the signal efficiently. Subspace Pursuit and Compressive Sampling Matching Pursuit are some of the popular greedy methods. Also, Fusion of Algorithms for Compressed Sensing is a recently proposed method where several CS reconstruction algorithms participate and the final estimate of the underlying sparse signal is determined by fusing the estimates obtained from the participating algorithms. All these methods involve solving a least squares problem which may be ill-conditioned, especially in the low dimension measurement regime. In this paper, we propose a step prior to least squares to ensure the well-conditioning of the least squares problem. Using Monte Carlo simulations, we show that in low dimension measurement scenario, this modification improves the reconstruction capability of the algorithm in clean as well as noisy measurement cases.

Item Type:	Conference Proceedings
Series.:	National Conference on Communications NCC
Publisher:	IEEE
Additional Information:	20th National Conference on Communications (NCC), Kanpur, INDIA, FEB 28-MAR 02, 2014
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited:	19 Jul 2015 09:30
Last Modified:	19 Jul 2015 09:30
URI:	http://eprints.iisc.ac.in/id/eprint/51852

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