Sparse Nonlinear Electromagnetic Imaging Accelerated With Projected Steepest Descent Algorithm
Type
ArticleAuthors
Desmal, Abdulla
Bagci, Hakan

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionElectrical Engineering Program
Date
2017-04-03Online Publication Date
2017-04-03Print Publication Date
2017-07Permanent link to this record
http://hdl.handle.net/10754/623106
Metadata
Show full item recordAbstract
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.Additional Links
http://ieeexplore.ieee.org/document/7891587/ae974a485f413a2113503eed53cd6c53
10.1109/TGRS.2017.2681184