Enhanced multilevel linear sampling methods for inverse scattering problems

Authors:
Jingzhi Li;Hongyu Liu;Qi Wang
Affiliations:
Faculty of Science, South University of Science and Technology of China, Shenzhen, 518055, PR China;Department of Mathematics and Statistics, University of North Carolina, Charlotte, NC 28223, USA;Department of Computing Sciences, School of Mathematics and Statistics, Xian Jiaotong University, Xian 710049, PR China
Venue:
Journal of Computational Physics
Year:
2014

Citing 9
Cited 0

A modified gradient method for two-dimensional problems in tomography

Journal of Computational and Applied Mathematics - Special issue on inverse problems in scattering theory
A Regularized Sampling Method for Solving Three-Dimensional Inverse Scattering Problems

SIAM Journal on Scientific Computing
Iterative forward and inverse algorithms based on domain integral equations for three-dimensional electric and magnetic objects

Journal of Computational Physics
Fast numerical solution of the electromagnetic medium scattering problem and applications to the inverse problem

Journal of Computational Physics
MUSIC-Type Electromagnetic Imaging of a Collection of Small Three-Dimensional Inclusions

SIAM Journal on Scientific Computing
Multilevel Linear Sampling Method for Inverse Scattering Problems

SIAM Journal on Scientific Computing
Postprocessing of the Linear Sampling Method by Means of Deformable Models

SIAM Journal on Scientific Computing
Strengthened Linear Sampling Method with a Reference Ball

SIAM Journal on Scientific Computing
The Linear Sampling Method in Inverse Electromagnetic Scattering

The Linear Sampling Method in Inverse Electromagnetic Scattering

Quantified Score

Hi-index	31.45

Visualization

Abstract

We develop two enhanced techniques for the multilevel linear sampling method (MLSM) proposed in [32] for inverse scattering problems. Under some practical situations, the MLSM suffers certain undesirable ''breakage cells'' problem. We propose to avoid the curse of ''breakage cells'' by incorporating ''expanding'' and ''searching'' techniques. The new techniques are shown to significantly improve the robustness of the MLSM, and meanwhile they possess the same optimal computational complexity as the MLSM. Numerical experiments are presented to illustrate the promising features of the enhanced MLSMs.