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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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. |
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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 |
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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. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Gai, Jianxin. Fu, Ping. Li, Zhen. Qiao, Jiaqing. |
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Article |
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Gai, Jianxin. Fu, Ping. Li, Zhen. Qiao, Jiaqing. |
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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 |
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2013 |
url |
https://hdl.handle.net/10356/85066 http://hdl.handle.net/10220/12026 |
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