Estimation of Electrical Characteristics of Complex Walls Using Deep Neural Networks

TL;DR

This paper introduces deep neural network models to accurately estimate complex wall electromagnetic properties from scattered wave data, aiding in deconvolving wall effects in radar signatures.

Contribution

It demonstrates the effectiveness of deep learning, including GANs, for nonlinear electromagnetic inverse scattering of inhomogeneous walls using simulated and real data.

Findings

Neural networks achieve ~95% accuracy in wall characterization.

Both single and dual network architectures are effective.

Models trained on simulation data generalize well to real-world data.

Abstract

Electromagnetic wave propagation through complex inhomogeneous walls introduces significant distortions to through-wall radar signatures. Estimation of wall thickness, dielectric, and conductivity profiles may enable wall effects to be deconvolved from target scattering. We propose to use deep neural networks (DNNs) to estimate wall characteristics from broadband scattered electric fields on the same side of the wall as the transmitter. We demonstrate that both single deep artificial and convolutional neural networks and dual networks involving generative adversarial networks are capable of performing the highly nonlinear regression operation of electromagnetic inverse scattering for wall characterization. These networks are trained with simulation data generated from full wave solvers and validated on both simulated and real wall data with approximately 95% accuracy.