RNN-Based Multistatic Scattered-Field Processing for the Solution of Electromagnetic Imaging Problems in Multilayer Environments

A processing method based on a recurrent neural network is proposed to compensate the effects due to the inconsistency between the actual measurement configuration and the simplified settings modelled within a quantitative electromagnetic imaging algorithm. The method is applied to the scattered electric field acquired in a multistatic setup, before giving it as the input of a multi-frequency inexact-Newton imaging procedure formulated in variable-exponent Lebesgue spaces. The approach is applied here for the first time to the imaging of dielectric targets inside a multilayer environment.