The linear sampling method for data generated by small random scatterers

Fuente: arXiv
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Auteurs principaux: Garnier, J., Haddar, H., Montanelli, H.
Format: Preprint
Publié: 2024
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author Garnier, J.
Haddar, H.
Montanelli, H.
author_facet Garnier, J.
Haddar, H.
Montanelli, H.
contents We present an extension of the linear sampling method for solving the sound-soft inverse scattering problem in two dimensions with data generated by randomly distributed small scatterers. The theoretical justification of our novel sampling method is based on a rigorous asymptotic model, a modified Helmholtz--Kirchhoff identity, and our previous work on the linear sampling method for random sources. Our numerical implementation incorporates boundary elements, Singular Value Decomposition, Tikhonov regularization, and Morozov's discrepancy principle. We showcase the robustness and accuracy of our algorithms with a series of numerical experiments.
format Preprint
id arxiv_https___arxiv_org_abs_2403_19482
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle The linear sampling method for data generated by small random scatterers
Garnier, J.
Haddar, H.
Montanelli, H.
Numerical Analysis
We present an extension of the linear sampling method for solving the sound-soft inverse scattering problem in two dimensions with data generated by randomly distributed small scatterers. The theoretical justification of our novel sampling method is based on a rigorous asymptotic model, a modified Helmholtz--Kirchhoff identity, and our previous work on the linear sampling method for random sources. Our numerical implementation incorporates boundary elements, Singular Value Decomposition, Tikhonov regularization, and Morozov's discrepancy principle. We showcase the robustness and accuracy of our algorithms with a series of numerical experiments.
title The linear sampling method for data generated by small random scatterers
topic Numerical Analysis
url https://arxiv.org/abs/2403.19482