Signal recovery from multiple measurement vectors via tunable random projection and boost

The problem of recovering a sparse solution from Multiple Measurement Vectors (MMVs) is a fundamental issue in the field of signal processing. However, the performance of existing recovery algorithms is far from satisfactory in terms of maximum recoverable sparsity level and minimum number of measur...

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Main Authors: Gai, Jianxin., Fu, Ping., Li, Zhen., Qiao, Jiaqing.
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2013
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Online Access:https://hdl.handle.net/10356/85066
http://hdl.handle.net/10220/12026
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-850662020-03-07T13:57:24Z Signal recovery from multiple measurement vectors via tunable random projection and boost Gai, Jianxin. Fu, Ping. Li, Zhen. Qiao, Jiaqing. School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering The problem of recovering a sparse solution from Multiple Measurement Vectors (MMVs) is a fundamental issue in the field of signal processing. However, the performance of existing recovery algorithms is far from satisfactory in terms of maximum recoverable sparsity level and minimum number of measurements required. In this paper, we present a high-performance recovery method which mainly has two parts: a versatile recovery framework named RPMB and a high-performance algorithm for it. Specifically, the RPMB framework improves the recovery performance by randomly projecting MMV onto a subspace with lower and tunable dimension in an iterative procedure. RPMB provides a generalized framework in which the popular ReMBo (Reduce MMV and Boost) algorithm can be regarded as a special case. Furthermore, an effective algorithm that can be embedded in RPMB is also proposed based on a new support identification strategy. Numerical experiments demonstrate that the proposed method outperforms state-of-the-art methods in terms of recovery performance. 2013-07-23T03:03:28Z 2019-12-06T15:56:28Z 2013-07-23T03:03:28Z 2019-12-06T15:56:28Z 2012 2012 Journal Article Gai, J., Fu, P., Li, Z., & Qiao, J. (2012). Signal recovery from multiple measurement vectors via tunable random projection and boost. Signal Processing, 92(12), 2901-2908. 0165-1684 https://hdl.handle.net/10356/85066 http://hdl.handle.net/10220/12026 10.1016/j.sigpro.2012.05.022 en Signal processing © 2012 Elsevier B.V.
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Gai, Jianxin.
Fu, Ping.
Li, Zhen.
Qiao, Jiaqing.
Signal recovery from multiple measurement vectors via tunable random projection and boost
description The problem of recovering a sparse solution from Multiple Measurement Vectors (MMVs) is a fundamental issue in the field of signal processing. However, the performance of existing recovery algorithms is far from satisfactory in terms of maximum recoverable sparsity level and minimum number of measurements required. In this paper, we present a high-performance recovery method which mainly has two parts: a versatile recovery framework named RPMB and a high-performance algorithm for it. Specifically, the RPMB framework improves the recovery performance by randomly projecting MMV onto a subspace with lower and tunable dimension in an iterative procedure. RPMB provides a generalized framework in which the popular ReMBo (Reduce MMV and Boost) algorithm can be regarded as a special case. Furthermore, an effective algorithm that can be embedded in RPMB is also proposed based on a new support identification strategy. Numerical experiments demonstrate that the proposed method outperforms state-of-the-art methods in terms of recovery performance.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Gai, Jianxin.
Fu, Ping.
Li, Zhen.
Qiao, Jiaqing.
format Article
author Gai, Jianxin.
Fu, Ping.
Li, Zhen.
Qiao, Jiaqing.
author_sort Gai, Jianxin.
title Signal recovery from multiple measurement vectors via tunable random projection and boost
title_short Signal recovery from multiple measurement vectors via tunable random projection and boost
title_full Signal recovery from multiple measurement vectors via tunable random projection and boost
title_fullStr Signal recovery from multiple measurement vectors via tunable random projection and boost
title_full_unstemmed Signal recovery from multiple measurement vectors via tunable random projection and boost
title_sort signal recovery from multiple measurement vectors via tunable random projection and boost
publishDate 2013
url https://hdl.handle.net/10356/85066
http://hdl.handle.net/10220/12026
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