A computational geometry method for the inverse scattering problem

TL;DR

This paper introduces a computational geometry-based Bayesian method to solve the inverse scattering problem for 2D star-shaped obstacles, accurately retrieving their support and refractive index from near field data.

Contribution

The paper presents a novel approach combining computational geometry and Bayesian modeling to solve the inverse scattering problem for non-convex, star-shaped obstacles.

Findings

Method reliably retrieves the support and refractive index.

Sampling approach is robust for estimating the scatterer's area.

Approach is adaptable to generalizations.

Abstract

In this paper we demonstrate a computational method to solve the inverse scattering problem for a star-shaped, smooth, penetrable obstacle in 2D. Our method is based on classical ideas from computational geometry. First, we approximate the support of a scatterer by a point cloud. Secondly, we use the Bayesian paradigm to model the joint conditional probability distribution of the non-convex hull of the point cloud and the constant refractive index of the scatterer given near field data. Of note, we use the non-convex hull of the point cloud as spline control points to evaluate, on a finer mesh, the volume potential arising in the integral equation formulation of the direct problem. Finally, in order to sample the arising posterior distribution, we propose a probability transition kernel that commutes with affine transformations of space. Our findings indicate that our method is reliable to retrieve the support and constant refractive index of the scatterer simultaneously. Indeed, our sampling method is robust to estimate a quantity of interest such as the area of the scatterer. We conclude pointing out a series of generalizations of our method.