Fast Correlation Method for Partial Fourier and Hadamard Sensing Matrices in Matching Pursuit Algorithms

Kee-Hoon KIM; Hosung PARK; Seokbeom HONG; Jong-Seon NO

doi:10.1587/transfun.E97.A.1674

Fast Correlation Method for Partial Fourier and Hadamard Sensing Matrices in Matching Pursuit Algorithms

Kee-Hoon KIM, Hosung PARK, Seokbeom HONG, Jong-Seon NO

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Summary :

There have been many matching pursuit algorithms (MPAs) which handle the sparse signal recovery problem, called compressed sensing (CS). In the MPAs, the correlation step makes a dominant computational complexity. In this paper, we propose a new fast correlation method for the MPA when we use partial Fourier sensing matrices and partial Hadamard sensing matrices which are widely used as the sensing matrix in CS. The proposed correlation method can be applied to almost all MPAs without causing any degradation of their recovery performance. Also, the proposed correlation method can reduce the computational complexity of the MPAs well even though there are restrictions depending on a used MPA and parameters.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E97-A No.8 pp.1674-1679

Publication Date: 2014/08/01

Publicized

Online ISSN: 1745-1337

DOI: 10.1587/transfun.E97.A.1674

Type of Manuscript: PAPER

Category: Digital Signal Processing

Authors

Kee-Hoon KIM
  Seoul National University
Hosung PARK
  Seoul National University
Seokbeom HONG
  Samsung Electric, Co. Ltd.
Jong-Seon NO
  Seoul National University

Keyword

compressed sensing, Fourier, Hadamard, low-complexity, matching pursuit, sensing matrix

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Kee-Hoon KIM, Hosung PARK, Seokbeom HONG, Jong-Seon NO, "Fast Correlation Method for Partial Fourier and Hadamard Sensing Matrices in Matching Pursuit Algorithms" in IEICE TRANSACTIONS on Fundamentals, vol. E97-A, no. 8, pp. 1674-1679, August 2014, doi: 10.1587/transfun.E97.A.1674.
Abstract: There have been many matching pursuit algorithms (MPAs) which handle the sparse signal recovery problem, called compressed sensing (CS). In the MPAs, the correlation step makes a dominant computational complexity. In this paper, we propose a new fast correlation method for the MPA when we use partial Fourier sensing matrices and partial Hadamard sensing matrices which are widely used as the sensing matrix in CS. The proposed correlation method can be applied to almost all MPAs without causing any degradation of their recovery performance. Also, the proposed correlation method can reduce the computational complexity of the MPAs well even though there are restrictions depending on a used MPA and parameters.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/transfun.E97.A.1674/_p

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@ARTICLE{e97-a_8_1674,
author={Kee-Hoon KIM, Hosung PARK, Seokbeom HONG, Jong-Seon NO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Fast Correlation Method for Partial Fourier and Hadamard Sensing Matrices in Matching Pursuit Algorithms},
year={2014},
volume={E97-A},
number={8},
pages={1674-1679},
abstract={There have been many matching pursuit algorithms (MPAs) which handle the sparse signal recovery problem, called compressed sensing (CS). In the MPAs, the correlation step makes a dominant computational complexity. In this paper, we propose a new fast correlation method for the MPA when we use partial Fourier sensing matrices and partial Hadamard sensing matrices which are widely used as the sensing matrix in CS. The proposed correlation method can be applied to almost all MPAs without causing any degradation of their recovery performance. Also, the proposed correlation method can reduce the computational complexity of the MPAs well even though there are restrictions depending on a used MPA and parameters.},
keywords={},
doi={10.1587/transfun.E97.A.1674},
ISSN={1745-1337},
month={August},}

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TY - JOUR
TI - Fast Correlation Method for Partial Fourier and Hadamard Sensing Matrices in Matching Pursuit Algorithms
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1674
EP - 1679
AU - Kee-Hoon KIM
AU - Hosung PARK
AU - Seokbeom HONG
AU - Jong-Seon NO
PY - 2014
DO - 10.1587/transfun.E97.A.1674
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E97-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2014
AB - There have been many matching pursuit algorithms (MPAs) which handle the sparse signal recovery problem, called compressed sensing (CS). In the MPAs, the correlation step makes a dominant computational complexity. In this paper, we propose a new fast correlation method for the MPA when we use partial Fourier sensing matrices and partial Hadamard sensing matrices which are widely used as the sensing matrix in CS. The proposed correlation method can be applied to almost all MPAs without causing any degradation of their recovery performance. Also, the proposed correlation method can reduce the computational complexity of the MPAs well even though there are restrictions depending on a used MPA and parameters.
ER -