A computational geometry method for the inverse scattering problem
This work addresses the inverse scattering problem for penetrable obstacles, offering a novel computational geometry-based approach, but it is incremental as it focuses on a specific 2D case with limited validation.
The paper presents a computational geometry method for solving the inverse scattering problem for a star-shaped, smooth, penetrable obstacle in 2D, simultaneously retrieving the support and constant refractive index. The method reliably estimates the scatterer's area using a Bayesian approach with an affine-invariant transition kernel.
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.