Signal recovery from multiple measurement vectors via tunable random projection and boost
Autor: | Jianxin Gai, Jiaqing Qiao, Ping Fu, Zhen Li |
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Přispěvatelé: | School of Electrical and Electronic Engineering |
Rok vydání: | 2012 |
Předmět: |
Mathematical optimization
Signal processing Computer science Random projection Field (computer science) Identification (information) Compressed sensing Dimension (vector space) Control and Systems Engineering Engineering::Electrical and electronic engineering [DRNTU] Signal Processing Computer Vision and Pattern Recognition Electrical and Electronic Engineering Algorithm Software Subspace topology |
Zdroj: | Signal Processing. 92:2901-2908 |
ISSN: | 0165-1684 |
DOI: | 10.1016/j.sigpro.2012.05.022 |
Popis: | 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. |
Databáze: | OpenAIRE |
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