Fast regularized linear sampling for inverse scattering problems

M. Fares; Serge Gratton; Philippe Toint

doi:10.1002/nla.698

Fast regularized linear sampling for inverse scattering problems

M. Fares, Serge Gratton, Philippe Toint

Namur Institute for Complex Systems

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Résumé

A new numerical procedure is proposed for the reconstruction of the shape and volume of unknown objects from measurements of their radiation in the far field. This procedure is a variant and the linear sampling method has a very acceptable computational load and is fully automated. It is based on exploiting an iteratively computed truncated singular-value decomposition and heuristics to extract the desired signal from the background noise. Its performance on a battery of examples of different types is shown to be promising. © 2010 John Wiley & Sons, Ltd.

langue originale	Anglais
Pages (de - à)	55-68
Nombre de pages	14
journal	Numerical Linear Algebra with Applications
Volume	18
Numéro de publication	1
Les DOIs	https://doi.org/10.1002/nla.698
Etat de la publication	Publié - 1 janv. 2011

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ADALGOPT: ADALGOPT - Algorithmes avancés en optimisation non-linéaire
Sartenaer, A. & TOINT, P.
1/01/87 → …
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abstract = "A new numerical procedure is proposed for the reconstruction of the shape and volume of unknown objects from measurements of their radiation in the far field. This procedure is a variant and the linear sampling method has a very acceptable computational load and is fully automated. It is based on exploiting an iteratively computed truncated singular-value decomposition and heuristics to extract the desired signal from the background noise. Its performance on a battery of examples of different types is shown to be promising. {\textcopyright} 2010 John Wiley & Sons, Ltd.",

keywords = " linear sampling method, regularization heuristics, numerical algorithms., inverse scattering",

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AB - A new numerical procedure is proposed for the reconstruction of the shape and volume of unknown objects from measurements of their radiation in the far field. This procedure is a variant and the linear sampling method has a very acceptable computational load and is fully automated. It is based on exploiting an iteratively computed truncated singular-value decomposition and heuristics to extract the desired signal from the background noise. Its performance on a battery of examples of different types is shown to be promising. © 2010 John Wiley & Sons, Ltd.

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KW - regularization heuristics

KW - numerical algorithms.

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Fast regularized linear sampling for inverse scattering problems

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