A Bayesian approach for solving inverse scattering from microwave laboratory-controlled data

Abstract

This paper deals with the reconstruction of two-dimensional objects from laboratory-controlled data in microwave tomography. This inverse problem is commonly ill-posed and nonlinear, therefore we propose to solve it in a Bayesian estimation framework using an iterative scheme to solve the optimization problem. This approach allows us to introduce a priori knowledge about the object function to be reconstructed. The experimental data were obtained in a controlled environment at Institut Fresnel (Marseille, France). The considered targets are either metallic or dielectric homogeneous cylinders. In this paper, the authors have only considered the data corresponding to the transverse magnetic polarization case. For these targets, the presented results show the potentiality of the proposed regularization scheme and the interest of these experimental data for testing inverse algorithms.