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

Authors:
Jianxin Gai;Ping Fu;Zhen Li;Jiaqing Qiao
Affiliations:
Department of Automatic Test and Control, Harbin Institute of Technology, Harbin, China and The Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentations of Heilong ...;Department of Automatic Test and Control, Harbin Institute of Technology, Harbin, China;School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore;Department of Automatic Test and Control, Harbin Institute of Technology, Harbin, China
Venue:
Signal Processing
Year:
2012

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Abstract

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.