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    Sparse Nonlinear Electromagnetic Imaging Accelerated With Projected Steepest Descent Algorithm

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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
    2017-04-03
    Online Publication Date
    2017-04-03
    Print Publication Date
    2017-07
    Permanent link to this record
    http://hdl.handle.net/10754/623106
    
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    Abstract
    An efficient electromagnetic inversion scheme for imaging sparse 3-D domains is proposed. The scheme achieves its efficiency and accuracy by integrating two concepts. First, the nonlinear optimization problem is constrained using L₀ or L₁-norm of the solution as the penalty term to alleviate the ill-posedness of the inverse problem. The resulting Tikhonov minimization problem is solved using nonlinear Landweber iterations (NLW). Second, the efficiency of the NLW is significantly increased using a steepest descent algorithm. The algorithm uses a projection operator to enforce the sparsity constraint by thresholding the solution at every iteration. Thresholding level and iteration step are selected carefully to increase the efficiency without sacrificing the convergence of the algorithm. Numerical results demonstrate the efficiency and accuracy of the proposed imaging scheme in reconstructing sparse 3-D dielectric profiles.
    Citation
    Desmal A, Bagci H (2017) Sparse Nonlinear Electromagnetic Imaging Accelerated With Projected Steepest Descent Algorithm. IEEE Transactions on Geoscience and Remote Sensing: 1–13. Available: http://dx.doi.org/10.1109/TGRS.2017.2681184.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Transactions on Geoscience and Remote Sensing
    DOI
    10.1109/TGRS.2017.2681184
    Additional Links
    http://ieeexplore.ieee.org/document/7891587/
    ae974a485f413a2113503eed53cd6c53
    10.1109/TGRS.2017.2681184
    Scopus Count
    Collections
    Articles; Electrical Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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