Simultaneous Estimation of Wall and Object Parameters in TWR Using Deep Neural Network

Abstract

The purpose of this paper is to present a deep learning model that simultaneously estimates targets and wall parameters in through-the-wall radar (TWR). As a result of the complexity of the environments in which through-the-wall radars operate, TWR faces many challenges. The propagation of radar signals through walls is further delayed and attenuated than in free space. Therefore, the targets are less able to be detected and the images of the targets are distorted and defocused as a consequence. To address the above challenges, two modes are considered in this work: single targets and two targets. In both cases, permittivity and wall thickness are considered, along with the target’s center in two dimensions and the permittivity of targets. Therefore, in the case of a single target, we estimate five values, whereas in the case of two targets, we estimate eight values simultaneously, each representing the mentioned parameters. As a result of using deep neural networks to solve the task of target locating problem in TWR, the model has a better chance of learning and increased accuracy if it involves more parameters (such as wall parameters and permittivity of the wall) in the target location problem. In this way, the accuracy of target locating improved when two wall parameters were considered in problem. A deep neural network model was used to estimate wall permittivity and thickness, as well as two-dimensional coordinates and permittivity of targets with 99% accuracy in single-target and two-target modes.

Microwave bone fracture diagnosis using deep neural network

“…Deep learning, which is a branch of machine learning that emulates the various activities of a human brain, can automatically learn the association between input and target data from past experiences 32 – 35 . With neural network architectures, basic principles can be analyzed based on previously provided data, and then, for different inputs, reasonable conclusions can be drawn 36 – 40 .…”

A novel technique for wall parameter estimation for through wall imaging systems

“…Machine learning based methods have been shown to produce quick and precise results but are less reliable as only simulation-based validations are presented. [10][11][12][13][14] Moreover, these models are effective for one and two targets but are inapplicable with multiple targets. A comparison between different machine learning methods is described in Table 1.…”

“…Few studies have explored methods for estimating wall parameters using machine learning. Machine learning based methods have been shown to produce quick and precise results but are less reliable as only simulation‐based validations are presented 10–14 . Moreover, these models are effective for one and two targets but are inapplicable with multiple targets.…”

Parking space number detection with multi‐branch convolution attention

“…Among them, "Intelligent Parking Space Detection System Based on Image Processing" [3] and "Robust Parking Space Detection Considering Inter-Space Correlation" [4] propose intelligent parking space detection systems that consider the correlation and robustness of parking spaces. Meanwhile, "Simultaneous Estimation of Wall and Object Parameters in TWR Using Deep Neural Network" [5] explores a method for simultaneously estimating wall and object parameters using deep neural networks. These studies are of great significance for improving urban parking management and reducing traffic congestion.…”