• DocumentCode
    1143502
  • Title

    A Robust Algorithm for Joint-Sparse Recovery

  • Author

    Hyder, Md Mashud ; Mahata, Kaushik

  • Author_Institution
    Dept. of Electr. Eng., Univ. of Newcastle, Callaghan, NSW, Australia
  • Volume
    16
  • Issue
    12
  • fYear
    2009
  • Firstpage
    1091
  • Lastpage
    1094
  • Abstract
    We address the problem of finding a set of sparse signals that have nonzero coefficients in the same locations from a set of their compressed measurements. A mixed lscr2,0 norm optimization approach is considered. A cost function appropriate to the joint-sparse problem is developed, and an algorithm is derived. Compared to other convex relaxation based techniques, the results obtained by the proposed method show a clear improvement in both noiseless and noisy environments.
  • Keywords
    convex programming; signal processing; sparse matrices; compressed measurements; convex relaxation based techniques; joint-sparse recovery; noiseless environment; noisy environment; nonzero coefficients; norm optimization approach; robust algorithm; sparse signals; Basis pursuit; compressive sampling; joint-sparse; multiple measurement vectors; sparse representation;
  • fLanguage
    English
  • Journal_Title
    Signal Processing Letters, IEEE
  • Publisher
    ieee
  • ISSN
    1070-9908
  • Type

    jour

  • DOI
    10.1109/LSP.2009.2028107
  • Filename
    5170022
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1143502