Deep learning based estimation of wall parameters for through-the-wall imaging
This project explores the use of deep learning algorithms, particularly Convolutional Neural Networks (CNNs), to estimate wall parameters in Through-the-Wall Imaging (TWI). TWI utilizes Ground Penetrating Radar (GPR) to detect objects hidden behind walls, but distinguishing between the target and cl...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
Nanyang Technological University
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/181594 |
| _version_ | 1826120894449188864 |
|---|---|
| author | Joseph, Christian |
| author2 | Abdulkadir C. Yucel |
| author_facet | Abdulkadir C. Yucel Joseph, Christian |
| author_sort | Joseph, Christian |
| collection | NTU |
| description | This project explores the use of deep learning algorithms, particularly Convolutional Neural Networks (CNNs), to estimate wall parameters in Through-the-Wall Imaging (TWI). TWI utilizes Ground Penetrating Radar (GPR) to detect objects hidden behind walls, but distinguishing between the target and clutter from the wall can be quite challenging. Traditional signal processing techniques often struggle with complex wall structures, which results to detection inaccuracies.
This project uses GPRMax software, an open-source radar simulation software, to create a dataset of synthetic B-scan images. CNN models are then developed using TensorFlow, which are trained on different datasets to learn the relationship between the B-scan data and simulation parameters. This project begins by estimating wall parameters and then expands to include the prediction of object parameters such as position and permittivity. |
| first_indexed | 2025-03-09T12:49:55Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/181594 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-03-09T12:49:55Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1815942024-12-13T15:45:39Z Deep learning based estimation of wall parameters for through-the-wall imaging Joseph, Christian Abdulkadir C. Yucel School of Electrical and Electronic Engineering acyucel@ntu.edu.sg Engineering This project explores the use of deep learning algorithms, particularly Convolutional Neural Networks (CNNs), to estimate wall parameters in Through-the-Wall Imaging (TWI). TWI utilizes Ground Penetrating Radar (GPR) to detect objects hidden behind walls, but distinguishing between the target and clutter from the wall can be quite challenging. Traditional signal processing techniques often struggle with complex wall structures, which results to detection inaccuracies. This project uses GPRMax software, an open-source radar simulation software, to create a dataset of synthetic B-scan images. CNN models are then developed using TensorFlow, which are trained on different datasets to learn the relationship between the B-scan data and simulation parameters. This project begins by estimating wall parameters and then expands to include the prediction of object parameters such as position and permittivity. Bachelor's degree 2024-12-10T06:30:56Z 2024-12-10T06:30:56Z 2024 Final Year Project (FYP) Joseph, C. (2024). Deep learning based estimation of wall parameters for through-the-wall imaging. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/181594 https://hdl.handle.net/10356/181594 en B3285-232 application/pdf Nanyang Technological University |
| spellingShingle | Engineering Joseph, Christian Deep learning based estimation of wall parameters for through-the-wall imaging |
| title | Deep learning based estimation of wall parameters for through-the-wall imaging |
| title_full | Deep learning based estimation of wall parameters for through-the-wall imaging |
| title_fullStr | Deep learning based estimation of wall parameters for through-the-wall imaging |
| title_full_unstemmed | Deep learning based estimation of wall parameters for through-the-wall imaging |
| title_short | Deep learning based estimation of wall parameters for through-the-wall imaging |
| title_sort | deep learning based estimation of wall parameters for through the wall imaging |
| topic | Engineering |
| url | https://hdl.handle.net/10356/181594 |
| work_keys_str_mv | AT josephchristian deeplearningbasedestimationofwallparametersforthroughthewallimaging |