Matching Pursuit Covariance Learning

Esa Ollila*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

Abstract

In the Multiple Measurements Vector (MMV) model, measurement vectors are connected to unknown, jointly sparse signal vectors through a linear regression model employing a single known measurement matrix (or dictionary). Typically, the number of atoms (columns of the dictionary) is greater than the number measurements and the sparse signal recovery problem is generally ill-posed. In this paper, we treat the signals and measurement noise as independent Gaussian random vectors with unknown signal covariance matrix and noise variance, respectively. Based on the negative log-likelihood function and maximum likelihood principle, we then introduce a matching pursuit covariance learning (CL) algorithm, analogous to popular orthogonal matching pursuit (OMP). Our numerical examples demonstrate effectiveness of the proposed CL strategy in sparse signal recovery where it performs favourably compared to the state-of-the-art algorithms under a broad variety of settings.

Original languageEnglish
Title of host publication32nd European Signal Processing Conference, EUSIPCO 2024 - Proceedings
PublisherIEEE
Pages2447-2451
Number of pages5
ISBN (Electronic)978-9-4645-9361-7
Publication statusPublished - 2024
MoE publication typeA4 Conference publication
EventEuropean Signal Processing Conference - Lyon, France
Duration: 26 Aug 202430 Aug 2024
Conference number: 32

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Conference

ConferenceEuropean Signal Processing Conference
Abbreviated titleEUSIPCO
Country/TerritoryFrance
CityLyon
Period26/08/202430/08/2024

Keywords

  • compressed sensing
  • orthogonal matching pursuit
  • sparse Bayesian learning
  • sparse signal reconstruction

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