Phaseless Microwave Imaging Of Dielectric Cylinders: An Artificial Neural Networks-Based Approach

Abstract

An inverse method for parameters estimation of dielectric cylinders (dielectric properties, location, and radius) from amplitude-only microwave information is presented. To this end two different Artificial Neural Networks (ANN) topologies were compared; a Multilayer Perceptron (MLP) and a Convolutional Neural Network (CNN). Several two-dimensional (2D) simulations, with different sizes and locations of homogeneous dielectric cylinders employing the Finite Differences Time Domain (FDTD) method, were performed to generate training, validation, and test sets for both ANN models. The prediction errors were lower for the CNN in high Signal-to-Noise Ratio (SNR) scenarios, although the MLP was more robust in low SNR situations. The CNN model performance was also tested for 2D simulations of dielectrically homogeneous and heterogeneous cylinders placed in acrylic holders showing potential experimental applications. Moreover, the CNN was also tested for a three-dimensional model simulated as realistic as possible, showing good results in predicting all parameters directly from the S-parameters.