Orthogonal least squares algorithm for the multiple-measurement vectors problem

Junhan Kim, Byonghyo Shim

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The multiple signal classification (MUSIC) algorithm, which was originally proposed to solve the direction of arrival (DOA) estimation problem in sensor array processing, has attracted much attention in recent years as a method to solve the multiple-measurement vectors (MMV) problem. While MUSIC reliably reconstructs the row sparse signals in the full row rank case, it performs poor in the rank deficient case. In order to overcome the limitation of MUSIC, we propose a robust greedy algorithm, henceforth referred to as an MMV orthogonal least squares (MMV-OLS) algorithm, for the MMV problem. Our analysis shows that in the full row rank case, MMV-OLS guarantees exact reconstruction of any row K-sparse signals from K + 1 measurements, which is in fact optimal since K + 1 is the smallest number of measurements to recover the row K-sparse matrices. In addition, we show that the recovery performance of MMV-OLS is competitive even in the rank deficient case by providing empirical results.

Original languageEnglish
Title of host publicationTENCON 2017 - 2017 IEEE Region 10 Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781509011339
Publication statusPublished - 2017 Dec 19
Externally publishedYes
Event2017 IEEE Region 10 Conference, TENCON 2017 - Penang, Malaysia
Duration: 2017 Nov 52017 Nov 8

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450


Other2017 IEEE Region 10 Conference, TENCON 2017

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering


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