A note on orthogonal matching pursuit under restricted isometry property
Abstract The orthogonal matching pursuit (OMP) algorithm is a classical greedy algorithm widely used in compressed sensing. The number of iterations required for the OMP algorithm to perform exact the recovery of sparse signals is a fundamental problem in signal processing. In this work, by investig...
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| Format: | Article |
| Language: | English |
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Wiley
2022-05-01
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| Series: | IET Signal Processing |
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| Online Access: | https://doi.org/10.1049/sil2.12096 |
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| _version_ | 1832559698680741888 |
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| author | Xueping Chen Jianzhong Liu Xianwen Ding Hengzhen Huang |
| author_facet | Xueping Chen Jianzhong Liu Xianwen Ding Hengzhen Huang |
| author_sort | Xueping Chen |
| collection | DOAJ |
| description | Abstract The orthogonal matching pursuit (OMP) algorithm is a classical greedy algorithm widely used in compressed sensing. The number of iterations required for the OMP algorithm to perform exact the recovery of sparse signals is a fundamental problem in signal processing. In this work, by investigating the relationship between the iteration number for OMP and the signal estimation error based on the restricted isometry property, the authors obtained tighter bounds on the number of iterations required to approximately recover a sparse signal with noise and exact support recovering for the noiseless cases of OMP. |
| format | Article |
| id | doaj-art-707144dc9f9f479791c62dbb881c848b |
| institution | Kabale University |
| issn | 1751-9675 1751-9683 |
| language | English |
| publishDate | 2022-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj-art-707144dc9f9f479791c62dbb881c848b2025-02-03T01:29:25ZengWileyIET Signal Processing1751-96751751-96832022-05-0116334035010.1049/sil2.12096A note on orthogonal matching pursuit under restricted isometry propertyXueping Chen0Jianzhong Liu1Xianwen Ding2Hengzhen Huang3School of Mathematics and Physics Jiangsu University of Technology Changzhou Jiangsu ChinaSchool of Mathematics and Physics Jiangsu University of Technology Changzhou Jiangsu ChinaSchool of Mathematics and Physics Jiangsu University of Technology Changzhou Jiangsu ChinaCollege of Mathematics and Statistics Guangxi Normal University Guilin Guangxi ChinaAbstract The orthogonal matching pursuit (OMP) algorithm is a classical greedy algorithm widely used in compressed sensing. The number of iterations required for the OMP algorithm to perform exact the recovery of sparse signals is a fundamental problem in signal processing. In this work, by investigating the relationship between the iteration number for OMP and the signal estimation error based on the restricted isometry property, the authors obtained tighter bounds on the number of iterations required to approximately recover a sparse signal with noise and exact support recovering for the noiseless cases of OMP.https://doi.org/10.1049/sil2.12096compressed sensingorthogonal matching pursuitorthogonal projection matrixrestricted isometry property (RIP)Wielandt inequality |
| spellingShingle | Xueping Chen Jianzhong Liu Xianwen Ding Hengzhen Huang A note on orthogonal matching pursuit under restricted isometry property IET Signal Processing compressed sensing orthogonal matching pursuit orthogonal projection matrix restricted isometry property (RIP) Wielandt inequality |
| title | A note on orthogonal matching pursuit under restricted isometry property |
| title_full | A note on orthogonal matching pursuit under restricted isometry property |
| title_fullStr | A note on orthogonal matching pursuit under restricted isometry property |
| title_full_unstemmed | A note on orthogonal matching pursuit under restricted isometry property |
| title_short | A note on orthogonal matching pursuit under restricted isometry property |
| title_sort | note on orthogonal matching pursuit under restricted isometry property |
| topic | compressed sensing orthogonal matching pursuit orthogonal projection matrix restricted isometry property (RIP) Wielandt inequality |
| url | https://doi.org/10.1049/sil2.12096 |
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