A Novel Sufficient Condition for Generalized Orthogonal Matching Pursuit
نویسندگان
چکیده
منابع مشابه
Correction to "Generalized Orthogonal Matching Pursuit"
As an extension of orthogonal matching pursuit (OMP) improving the recovery performance of sparse signals, generalized OMP (gOMP) has recently been studied in the literature. In this paper, we present a new analysis of the gOMP algorithm using restricted isometry property (RIP). We show that if the measurement matrix Φ ∈ R satisfies the RIP with δmax{9,S+1}K ≤ 1 8 , then gOMP performs stable re...
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Generalized orthogonal matching pursuit (gOMP), also called orthogonal multi-matching pursuit, is an extension of OMP in the sense that N ≥ 1 indices are identified per iteration. In this paper, we show that if the restricted isometry constant (RIC) δNK+1 of a sensing matrix A satisfies δNK+1 < 1/ √ K/N + 1, then under a condition on the signal-to-noise ratio, gOMP identifies at least one index...
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Generalized Orthogonal Matching Pursuit (gOMP) is a natural extension of OMP algorithm where unlike OMP, it may select N(≥ 1) atoms in each iteration. In this paper, we demonstrate that gOMP can successfully reconstruct a K-sparse signal from a compressed measurement y = Φx by K iteration if the sensing matrix Φ satisfies restricted isometry property (RIP) of order NK where δNK < √ N √ K+2 √ N ...
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Generalized orthogonal matching pursuit (gOMP) algorithm has received much attention in recent years as a natural extension of orthogonal matching pursuit. It is used to recover sparse signals in compressive sensing. In this paper, a new bound is obtained for the exact reconstruction of every K-sparse signal via the gOMP algorithm in the noiseless case. That is, if the restricted isometry const...
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Orthogonal matching pursuit (OMP) is a widely used compressive sensing (CS) algorithm for recovering sparse signals in noisy linear regression models. The performance of OMP depends on its stopping criteria (SC). SC for OMP discussed in literature typically assumes knowledge of either the sparsity of the signal to be estimated k0 or noise variance σ , both of which are unavailable in many pract...
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ژورنال
عنوان ژورنال: IEEE Communications Letters
سال: 2017
ISSN: 1089-7798
DOI: 10.1109/lcomm.2016.2642922