Simultaneous estimation of wall and object parameters in TWR using deep neural network

TL;DR

This paper introduces a deep learning approach for accurately estimating both wall and target parameters in Through-the-Wall Radar, improving target localization by jointly estimating wall permittivity, thickness, and target positions.

Contribution

The work demonstrates that including wall parameters in the neural network model enhances target localization accuracy in TWR systems.

Findings

Achieved 99% accuracy in estimating wall and target parameters.

Joint estimation of wall and target parameters improves localization accuracy.

Deep neural networks benefit from modeling more parameters for better performance.

Abstract

This paper presents a deep learning model for simultaneously estimating target and wall parameters in Through-the-Wall Radar. In this work, we consider two modes: single-target and two-targets. In both cases, we consider the permittivity and thickness for the wall, as well as the two-dimensional coordinates of the target's center and permittivity. This means that in the case of a single target, we estimate five values, whereas, in the case of two targets, we estimate eight values simultaneously, each of which represents the mentioned parameters. We discovered that when using deep neural networks to solve the target locating problem, giving the model more parameters of the problem increases the location accuracy. As a result, we included two wall parameters in the problem and discovered that the accuracy of target locating improves while the wall parameters are estimated. We were able to estimate the parameters of wall permittivity and thickness, as well as two-dimensional coordinates and permittivity of targets in single-target and two-target modes with 99\% accuracy by using a deep neural network model.