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    SPARSE ELECTROMAGNETIC IMAGING USING NONLINEAR LANDWEBER ITERATIONS

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    Type
    Article
    Authors
    Desmal, Abdulla cc
    Bagci, Hakan cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Date
    2015-10-07
    Online Publication Date
    2015-10-07
    Print Publication Date
    2015
    Permanent link to this record
    http://hdl.handle.net/10754/593268
    
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    Abstract
    A scheme for efficiently solving the nonlinear electromagnetic inverse scattering problem on sparse investigation domains is described. The proposed scheme reconstructs the (complex) dielectric permittivity of an investigation domain from fields measured away from the domain itself. Least-squares data misfit between the computed scattered fields, which are expressed as a nonlinear function of the permittivity, and the measured fields is constrained by the L0/L1-norm of the solution. The resulting minimization problem is solved using nonlinear Landweber iterations, where at each iteration a thresholding function is applied to enforce the sparseness-promoting L0/L1-norm constraint. The thresholded nonlinear Landweber iterations are applied to several two-dimensional problems, where the ``measured'' fields are synthetically generated or obtained from actual experiments. These numerical experiments demonstrate the accuracy, efficiency, and applicability of the proposed scheme in reconstructing sparse profiles with high permittivity values.
    Citation
    SPARSE ELECTROMAGNETIC IMAGING USING NONLINEAR LANDWEBER ITERATIONS 2015, 152:77 Progress In Electromagnetics Research
    Publisher
    EMW Publishing
    Journal
    Progress In Electromagnetics Research
    DOI
    10.2528/PIER15052806
    Additional Links
    http://www.jpier.org/PIER/pier.php?paper=15052806
    ae974a485f413a2113503eed53cd6c53
    10.2528/PIER15052806
    Scopus Count
    Collections
    Articles; Electrical Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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