کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
562653 875425 2012 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Signal recovery from multiple measurement vectors via tunable random projection and boost
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
پیش نمایش صفحه اول مقاله
Signal recovery from multiple measurement vectors via tunable random projection and boost
چکیده انگلیسی

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.


► A high-performance recovery algorithm is presented for recovery of the MMV problem.
► A tunable random projection framework is proposed and verified theoretically.
► This framework can be regarded as the generalization of ReMBo algorithm.
► An effective solver for the framework is proposed based on a new support identification strategy.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing - Volume 92, Issue 12, December 2012, Pages 2901–2908
نویسندگان
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