Analysis of Orthogonal Matching Pursuit for Compressed Sensing in Practical Settings

Conference paper (2023)

Authors

H. Masoumi Team Nitin Myers - Mechanical, Maritime and Materials Engineering
M.H.G. Verhaegen Team Michel Verhaegen - Mechanical, Maritime and Materials Engineering
N.J. Myers Team Nitin Myers - Mechanical, Maritime and Materials Engineering
Research Group
Team Nitin Myers (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2023 H. Masoumi, M.H.G. Verhaegen, N.J. Myers
DOI: https://doi.org/10.1109/SSP53291.2023.10207984
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Published Date

2023

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Nitin Myers

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.

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