Sparse signal recovery on the sphere: Optimizing the sensing matrix through sampling

Yibeltal F. Alem, Daniel H. Chae, Rodney A. Kennedy

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

    5 Citations (Scopus)

    Abstract

    We propose a method for constructing a spherical harmonic sensing matrix that can be used to effectively recover a sparse signal on the sphere from limited measurements. For such a sensing matrix, in a compressed sensing setting, it is desirable that the restricted isometry property (RIP) holds. Our sensing matrix is obtained by drawing random samples from a grid derived from minimal discrete energy spiral distribution of sample points (spiral scheme). This sampling scheme and construction of sampling matrix is shown to be superior to the use of preconditioned equiangular samples from the literature (regular scheme). Our numerical results show that the success rate in near exact recovery of a sparse coefficient vector with our spiral scheme is superior to that of the regular scheme over a range of SNRs.

    Original languageEnglish
    Title of host publication6th International Conference on Signal Processing and Communication Systems, ICSPCS 2012 - Proceedings
    DOIs
    Publication statusPublished - 2012
    Event6th International Conference on Signal Processing and Communication Systems, ICSPCS 2012 - Gold Coast, QLD, Australia
    Duration: 12 Dec 201214 Dec 2012

    Publication series

    Name6th International Conference on Signal Processing and Communication Systems, ICSPCS 2012 - Proceedings

    Conference

    Conference6th International Conference on Signal Processing and Communication Systems, ICSPCS 2012
    Country/TerritoryAustralia
    CityGold Coast, QLD
    Period12/12/1214/12/12

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