dc.contributor.authorGai, Jianxin.
dc.contributor.authorFu, Ping.
dc.contributor.authorLi, Zhen.
dc.contributor.authorQiao, Jiaqing.
dc.identifier.citationGai, 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.en_US
dc.description.abstractThe 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.en_US
dc.relation.ispartofseriesSignal processingen_US
dc.rights© 2012 Elsevier B.V.en_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering
dc.titleSignal recovery from multiple measurement vectors via tunable random projection and boosten_US
dc.typeJournal Article
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US

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