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

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 sig...

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Bibliographic Details
Published in:International journal of antennas and propagation 2022-12, Vol.2022, p.1-10
Main Authors: Ghorbani, Fardin, Soleimani, Hossein
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Artificial neural networks
Deep learning
Machine learning
Mathematical models
Methods
Neural networks
Neurons
Parameters
Permittivity
Wall thickness
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Summary: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.
ISSN:1687-5869
1687-5877
DOI:10.1155/2022/7810213