Drone localisation with machine learning
With the upward trend in sales and ownership of recreational drones to the public consumers in both the global and local context. This give rise to possible privacy implications caused by malicious intent of drone owners. This project aims to develop a system that both identify and localizes dron...
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Nanyang Technological University
2020
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sg-ntu-dr.10356-1397242023-07-07T18:53:19Z Drone localisation with machine learning Toh, Isaac Tan Soon Yim School of Electrical and Electronic Engineering Chee Kiat Seow ESYTAN@ntu.edu.sg Engineering::Electrical and electronic engineering With the upward trend in sales and ownership of recreational drones to the public consumers in both the global and local context. This give rise to possible privacy implications caused by malicious intent of drone owners. This project aims to develop a system that both identify and localizes drones within the desired monitoring vicinity enhanced with machine learning to improve identification accuracy. It presents an affordable and effective way to both identify and localize drones within detection range. The system helps users in protecting themselves from privacy implications caused by the presence of drones. Users will be more informed of the airspace within the detection range allowing for appropriate countermeasures to be taken. This report outlines the design and development process of the system. The main programming language used to create this project is Python, which handles all scripts in this project. The access point module from Python has aided the development of this project greatly, as it allows the interfacing with the operating system’s wireless card to be seamless. Hence, the primary purpose of the project has been created. The system itself has been tested and works properly with minor supervision from user due to the Machine learning aspect. Further implementations can be explored to further improve on the efficiency and effectiveness in identifying drones. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-05-21T05:14:30Z 2020-05-21T05:14:30Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/139724 en A3247-191 application/pdf application/octet-stream application/octet-stream Nanyang Technological University |
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Engineering::Electrical and electronic engineering Toh, Isaac Drone localisation with machine learning |
description |
With the upward trend in sales and ownership of recreational drones to the public
consumers in both the global and local context. This give rise to possible privacy
implications caused by malicious intent of drone owners. This project aims to develop
a system that both identify and localizes drones within the desired monitoring vicinity
enhanced with machine learning to improve identification accuracy. It presents an
affordable and effective way to both identify and localize drones within detection
range. The system helps users in protecting themselves from privacy implications
caused by the presence of drones. Users will be more informed of the airspace within
the detection range allowing for appropriate countermeasures to be taken.
This report outlines the design and development process of the system. The main
programming language used to create this project is Python, which handles all scripts
in this project. The access point module from Python has aided the development of this
project greatly, as it allows the interfacing with the operating system’s wireless card to
be seamless.
Hence, the primary purpose of the project has been created. The system itself has been
tested and works properly with minor supervision from user due to the Machine
learning aspect. Further implementations can be explored to further improve on the
efficiency and effectiveness in identifying drones. |
author2 |
Tan Soon Yim |
author_facet |
Tan Soon Yim Toh, Isaac |
format |
Final Year Project |
author |
Toh, Isaac |
author_sort |
Toh, Isaac |
title |
Drone localisation with machine learning |
title_short |
Drone localisation with machine learning |
title_full |
Drone localisation with machine learning |
title_fullStr |
Drone localisation with machine learning |
title_full_unstemmed |
Drone localisation with machine learning |
title_sort |
drone localisation with machine learning |
publisher |
Nanyang Technological University |
publishDate |
2020 |
url |
https://hdl.handle.net/10356/139724 |
_version_ |
1772828548309975040 |