Shrinkage-Thresholding Enhanced Born Iterative Method for Solving 2D Inverse Electromagnetic Scattering Problem

A numerical framework that incorporates recently developed iterative shrinkage thresholding (IST) algorithms within the Born iterative method (BIM) is proposed for solving the two-dimensional inverse electromagnetic scattering problem. IST algorithms minimize a cost function weighted between measure...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on antennas and propagation Vol. 62; no. 7; pp. 3878 - 3884
Main Authors: Desmal, Abdulla, Bagci, Hakan
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.07.2014
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Applied classical electromagnetism
Born iterative method
Cost function
Discontinuity
Electromagnetic scattering
Electromagnetic wave propagation, radiowave propagation
Electromagnetism; electron and ion optics
Equations
Exact sciences and technology
Fundamental areas of phenomenology (including applications)
Inverse
Iterative methods
iterative shrinkage thresholding algorithms
Mathematical models
microwave imaging
Minimization
Noise
Permittivity
Physics
regularization
Scattering
Two dimensional
Cost minimization
Electromagnetism
Threshold detection
Digital simulation
Inverse scattering method
regularization
Two dimensional model
Born approximation
Convergence speed
Algorithms
Born iterative method
iterative shrinkage thresholding algorithms
Convergence rate
Cost function
Iterative methods
Permittivity
Microwave imaging
ISSN:0018-926X, 1558-2221
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A numerical framework that incorporates recently developed iterative shrinkage thresholding (IST) algorithms within the Born iterative method (BIM) is proposed for solving the two-dimensional inverse electromagnetic scattering problem. IST algorithms minimize a cost function weighted between measurement-data misfit and a zeroth/first-norm penalty term and therefore promote "sharpness" in the solution. Consequently, when applied to domains with sharp variations, discontinuities, or sparse content, the proposed framework is more efficient and accurate than the "classical" BIM that minimizes a cost function with a second-norm penalty term. Indeed, numerical results demonstrate the superiority of the IST-BIM over the classical BIM when they are applied to sparse domains: Permittivity and conductivity profiles recovered using the IST-BIM are sharper and more accurate and converge faster.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Article-2
ObjectType-Feature-1
content type line 23
ISSN:0018-926X
1558-2221
DOI:10.1109/TAP.2014.2321144