Identification of multilayered particles from scattering data by a clustering method

TL;DR

This paper presents a clustering-based approach to identify multilayered particles from scattering data, utilizing local and global minimization techniques to solve the inverse scattering problem in complex scenarios.

Contribution

It introduces a Reduction Procedure to lower the problem's dimensionality and applies Deep's and Multilevel Single-Linkage methods for effective global minimization.

Findings

Reduction Procedure effectively reduces dimensionality.

Deep's and Multilevel Single-Linkage methods perform well.

Methods are validated on various multilayer configurations.

Abstract

A multilayered particle is illuminated by plane acoustic or electromagnetic waves of one or several frequencies. We consider the inverse scattering problem for the identification of the layers and of the refraction coefficients of the scatterer in a non-Born region of scattering. Local deterministic and global probabilistic minimization methods are studied. A special Reduction Procedure is introduced to reduce the dimensionality of the minimization space. Deep's and the Multilevel Single-Linkage methods for global minimization are used for the solution of the inverse problem. Their performance is analyzed for various multilayer configurations.