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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Bibliographic Details
Main Author: Toh, Isaac
Other Authors: Tan Soon Yim
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2020
Subjects:
Online Access:https://hdl.handle.net/10356/139724
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Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-139724
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spelling 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
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
spellingShingle 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
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