In compressed sensing, most previous researches have studied the recovery performance of a sparse signal x based on the acquired model y=Φx+n, where n denotes the noise vector. There are also related studies for general perturbation environment, i.e., y=(Φ+E)x+n, where E is the measurement perturbation. IHT and HTP algorithms are the classical algorithms for sparse signal reconstruction in compressed sensing. Under the general perturbations, this paper derive the required sufficient conditions and the error bounds of IHT and HTP algorithms.
Xiaobo ZHANG
Beijing University of Posts and Telecommunications
Wenbo XU
Beijing University of Posts and Telecommunications
Yupeng CUI
Beijing University of Posts and Telecommunications
Jiaru LIN
Beijing University of Posts and Telecommunications
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Copy
Xiaobo ZHANG, Wenbo XU, Yupeng CUI, Jiaru LIN, "Recovery Performance of IHT and HTP Algorithms under General Perturbations" in IEICE TRANSACTIONS on Fundamentals,
vol. E101-A, no. 10, pp. 1698-1702, October 2018, doi: 10.1587/transfun.E101.A.1698.
Abstract: In compressed sensing, most previous researches have studied the recovery performance of a sparse signal x based on the acquired model y=Φx+n, where n denotes the noise vector. There are also related studies for general perturbation environment, i.e., y=(Φ+E)x+n, where E is the measurement perturbation. IHT and HTP algorithms are the classical algorithms for sparse signal reconstruction in compressed sensing. Under the general perturbations, this paper derive the required sufficient conditions and the error bounds of IHT and HTP algorithms.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E101.A.1698/_p
Copy
@ARTICLE{e101-a_10_1698,
author={Xiaobo ZHANG, Wenbo XU, Yupeng CUI, Jiaru LIN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Recovery Performance of IHT and HTP Algorithms under General Perturbations},
year={2018},
volume={E101-A},
number={10},
pages={1698-1702},
abstract={In compressed sensing, most previous researches have studied the recovery performance of a sparse signal x based on the acquired model y=Φx+n, where n denotes the noise vector. There are also related studies for general perturbation environment, i.e., y=(Φ+E)x+n, where E is the measurement perturbation. IHT and HTP algorithms are the classical algorithms for sparse signal reconstruction in compressed sensing. Under the general perturbations, this paper derive the required sufficient conditions and the error bounds of IHT and HTP algorithms.},
keywords={},
doi={10.1587/transfun.E101.A.1698},
ISSN={1745-1337},
month={October},}
Copy
TY - JOUR
TI - Recovery Performance of IHT and HTP Algorithms under General Perturbations
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1698
EP - 1702
AU - Xiaobo ZHANG
AU - Wenbo XU
AU - Yupeng CUI
AU - Jiaru LIN
PY - 2018
DO - 10.1587/transfun.E101.A.1698
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E101-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 2018
AB - In compressed sensing, most previous researches have studied the recovery performance of a sparse signal x based on the acquired model y=Φx+n, where n denotes the noise vector. There are also related studies for general perturbation environment, i.e., y=(Φ+E)x+n, where E is the measurement perturbation. IHT and HTP algorithms are the classical algorithms for sparse signal reconstruction in compressed sensing. Under the general perturbations, this paper derive the required sufficient conditions and the error bounds of IHT and HTP algorithms.
ER -