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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2022-01-01
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| Series: | International Journal of Antennas and Propagation |
| Online Access: | http://dx.doi.org/10.1155/2022/7810213 |
| _version_ | 1797958233159106560 |
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| author | Fardin Ghorbani Hossein Soleimani |
| author_facet | Fardin Ghorbani Hossein Soleimani |
| author_sort | Fardin Ghorbani |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T00:16:04Z |
| format | Article |
| id | doaj.art-1903cdbdf9374a43a984153fd1ddf8e5 |
| institution | Directory Open Access Journal |
| issn | 1687-5877 |
| language | English |
| last_indexed | 2024-04-11T00:16:04Z |
| publishDate | 2022-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | International Journal of Antennas and Propagation |
| spelling | doaj.art-1903cdbdf9374a43a984153fd1ddf8e52023-01-09T01:30:11ZengHindawi LimitedInternational Journal of Antennas and Propagation1687-58772022-01-01202210.1155/2022/7810213Simultaneous Estimation of Wall and Object Parameters in TWR Using Deep Neural NetworkFardin Ghorbani0Hossein Soleimani1School of Electrical EngineeringSchool of Electrical EngineeringThe 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.http://dx.doi.org/10.1155/2022/7810213 |
| spellingShingle | Fardin Ghorbani Hossein Soleimani Simultaneous Estimation of Wall and Object Parameters in TWR Using Deep Neural Network International Journal of Antennas and Propagation |
| title | Simultaneous Estimation of Wall and Object Parameters in TWR Using Deep Neural Network |
| title_full | Simultaneous Estimation of Wall and Object Parameters in TWR Using Deep Neural Network |
| title_fullStr | Simultaneous Estimation of Wall and Object Parameters in TWR Using Deep Neural Network |
| title_full_unstemmed | Simultaneous Estimation of Wall and Object Parameters in TWR Using Deep Neural Network |
| title_short | Simultaneous Estimation of Wall and Object Parameters in TWR Using Deep Neural Network |
| title_sort | simultaneous estimation of wall and object parameters in twr using deep neural network |
| url | http://dx.doi.org/10.1155/2022/7810213 |
| work_keys_str_mv | AT fardinghorbani simultaneousestimationofwallandobjectparametersintwrusingdeepneuralnetwork AT hosseinsoleimani simultaneousestimationofwallandobjectparametersintwrusingdeepneuralnetwork |