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 volumeConference contributionScientificpeer-review


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 languageEnglish
Title of host publicationProceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023
ISBN (Electronic)978-1-6654-5245-8
Publication statusPublished - 2023
Event22nd IEEE Statistical Signal Processing Workshop, SSP 2023 - Hanoi, Viet Nam
Duration: 2 Jul 20235 Jul 2023


Conference22nd IEEE Statistical Signal Processing Workshop, SSP 2023
Country/TerritoryViet Nam

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project
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.


  • Compressive sensing
  • mutual coherence
  • orthogonal matching pursuit
  • support recovery

Cite this