Analysis of Orthogonal Matching Pursuit for Compressed Sensing in Practical Settings

Hamed Masoumi; Michel Verhaegen; Nitin Jonathan Myers

doi:10.1109/SSP53291.2023.10207984

Analysis of Orthogonal Matching Pursuit for Compressed Sensing in Practical Settings

Hamed Masoumi^*, Michel Verhaegen, Nitin Jonathan Myers

^*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

Abstract

Orthogonal matching pursuit (OMP) is a widely used greedy algorithm for sparse signal recovery in compressed sensing (CS). Prior work on OMP, however, has only provided reconstruction guarantees under the assumption that the columns of the CS matrix have equal norms, which is unrealistic in many practical CS applications due to hardware constraints. In this paper, we derive sparse recovery guarantees with OMP, when the CS matrix has unequal column norms. Finally, we show that CS matrices whose column norms are comparable achieve tight guarantees for the successful recovery of the support of a sparse signal and a low mean squared error in the estimate.

Original language	English
Title of host publication	Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023
Publisher	IEEE
Pages	170-174
ISBN (Electronic)	978-1-6654-5245-8
DOIs	https://doi.org/10.1109/SSP53291.2023.10207984
Publication status	Published - 2023
Event	22nd IEEE Statistical Signal Processing Workshop, SSP 2023 - Hanoi, Viet Nam Duration: 2 Jul 2023 → 5 Jul 2023

Conference

Conference	22nd IEEE Statistical Signal Processing Workshop, SSP 2023
Country/Territory	Viet Nam
City	Hanoi
Period	2/07/23 → 5/07/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

Compressive sensing
mutual coherence
orthogonal matching pursuit
support recovery

Access to Document

10.1109/SSP53291.2023.10207984

Cite this

@inproceedings{2af94223be794d718ad50b59b66c343c,

title = "Analysis of Orthogonal Matching Pursuit for Compressed Sensing in Practical Settings",

abstract = "Orthogonal matching pursuit (OMP) is a widely used greedy algorithm for sparse signal recovery in compressed sensing (CS). Prior work on OMP, however, has only provided reconstruction guarantees under the assumption that the columns of the CS matrix have equal norms, which is unrealistic in many practical CS applications due to hardware constraints. In this paper, we derive sparse recovery guarantees with OMP, when the CS matrix has unequal column norms. Finally, we show that CS matrices whose column norms are comparable achieve tight guarantees for the successful recovery of the support of a sparse signal and a low mean squared error in the estimate.",

keywords = "Compressive sensing, mutual coherence, orthogonal matching pursuit, support recovery",

author = "Hamed Masoumi and Michel Verhaegen and Myers, {Nitin Jonathan}",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; 22nd IEEE Statistical Signal Processing Workshop, SSP 2023 ; Conference date: 02-07-2023 Through 05-07-2023",

year = "2023",

doi = "10.1109/SSP53291.2023.10207984",

language = "English",

pages = "170--174",

booktitle = "Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023",

publisher = "IEEE ",

address = "United States",

}

Analysis of Orthogonal Matching Pursuit for Compressed Sensing in Practical Settings. / Masoumi, Hamed ; Verhaegen, Michel ; Myers, Nitin Jonathan.

Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023. IEEE , 2023. p. 170-174.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Analysis of Orthogonal Matching Pursuit for Compressed Sensing in Practical Settings

AU - Masoumi, Hamed

AU - Verhaegen, Michel

AU - Myers, Nitin Jonathan

N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2023

Y1 - 2023

N2 - Orthogonal matching pursuit (OMP) is a widely used greedy algorithm for sparse signal recovery in compressed sensing (CS). Prior work on OMP, however, has only provided reconstruction guarantees under the assumption that the columns of the CS matrix have equal norms, which is unrealistic in many practical CS applications due to hardware constraints. In this paper, we derive sparse recovery guarantees with OMP, when the CS matrix has unequal column norms. Finally, we show that CS matrices whose column norms are comparable achieve tight guarantees for the successful recovery of the support of a sparse signal and a low mean squared error in the estimate.

AB - Orthogonal matching pursuit (OMP) is a widely used greedy algorithm for sparse signal recovery in compressed sensing (CS). Prior work on OMP, however, has only provided reconstruction guarantees under the assumption that the columns of the CS matrix have equal norms, which is unrealistic in many practical CS applications due to hardware constraints. In this paper, we derive sparse recovery guarantees with OMP, when the CS matrix has unequal column norms. Finally, we show that CS matrices whose column norms are comparable achieve tight guarantees for the successful recovery of the support of a sparse signal and a low mean squared error in the estimate.

KW - Compressive sensing

KW - mutual coherence

KW - orthogonal matching pursuit

KW - support recovery

UR - http://www.scopus.com/inward/record.url?scp=85168909563&partnerID=8YFLogxK

U2 - 10.1109/SSP53291.2023.10207984

DO - 10.1109/SSP53291.2023.10207984

M3 - Conference contribution

AN - SCOPUS:85168909563

SP - 170

EP - 174

BT - Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023

PB - IEEE

T2 - 22nd IEEE Statistical Signal Processing Workshop, SSP 2023

Y2 - 2 July 2023 through 5 July 2023

ER -

Analysis of Orthogonal Matching Pursuit for Compressed Sensing in Practical Settings

Abstract

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Embargoed Document

Cite this