Better than ℓ0 recovery via blind identification

Richard J Porter, Vladislav Tadic, Alin Achim

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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Abstract

In this work, we propose a novel approach to multiple measurement vector (MMV) compressed sensing. We show that by exploiting the statistical properties of the sources, we can do better than previously derived lower bounds in this context. We show that in the MMV case, we can identify the active sources with fewer sensors than sources. We first develop a general framework for recovering the sparsity profile of the sources by combining ideas from compressed sensing with blind identification methods. We do this by comparing the large known sensing matrix to the smaller matrix estimated by a blind identification method. Finally, we demonstrate the performance of this technique with a variety of data and blind identification methods, and show that under certain assumptions, it is possible to identify the active sources with only 2 sensors, regardless of the number of sources.
Original languageEnglish
Title of host publication2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015
Subtitle of host publicationProceedings of a meeting held 14-16 December 2015, in Orlando, FL, USA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1280-1284
Number of pages5
ISBN (Electronic)9781479975914
ISBN (Print)9781479975921
DOIs
Publication statusPublished - 23 Feb 2016
EventIEEE Global Conference on Signal and Information Processing, GlobalSIP 2015 - Orlando, United States
Duration: 13 Dec 201516 Dec 2015

Conference

ConferenceIEEE Global Conference on Signal and Information Processing, GlobalSIP 2015
CountryUnited States
CityOrlando
Period13/12/1516/12/15

Keywords

  • Blind identification
  • Compressed Sensing

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