• DocumentCode
    19550
  • Title

    MUSIC Algorithms for Grid Diagnostics

  • Author

    Solimene, Raffaele ; Leone, Giovanni

  • Author_Institution
    Dipt. di Ing. dell´´Inf., Seconda Univ. di Napoli, Aversa, Italy
  • Volume
    10
  • Issue
    2
  • fYear
    2013
  • fDate
    Mar-13
  • Firstpage
    226
  • Lastpage
    230
  • Abstract
    The problem of detecting and localizing missing scatterers (faults) inside a known grid of small cross-sectional perfect-electric-conducting cylinders is dealt with. The case of a TM scalar 2-D geometry is considered, and the Multiple Signal Classification (MUSIC) spectral estimation technique is employed. Two different scattering models are employed and compared. The first one aims at localizing present objects within a free-space background medium. The second one aims at detecting the faults and exploits the Green´s function of the full grid. The limitations of the first approach are pointed out and connected to the maximum dimension of the data space. Then, the second approach performs successfully when the fault number is lower than scattering objects.
  • Keywords
    Green´s function methods; grid computing; signal classification; Green function; MUSIC algorithms; MUSIC spectral estimation technique; TM scalar 2D geometry; fault detection; fault localization; free-space background medium; grid diagnostics; missing scatterers; multiple signal classification; small cross-sectional perfect-electric-conducting cylinders; Green´s function methods; Inverse problems; Multiple signal classification; Noise; Optimized production technology; Scattering; Vectors; Grid diagnostics; Multiple Signal Classification (MUSIC) algorithm; microwave imaging; object localization;
  • fLanguage
    English
  • Journal_Title
    Geoscience and Remote Sensing Letters, IEEE
  • Publisher
    ieee
  • ISSN
    1545-598X
  • Type

    jour

  • DOI
    10.1109/LGRS.2012.2198043
  • Filename
    6221948