Abstract: Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. Besides their useful applications, an alarming concern in regards to the physical infrastructure security, safety and privacy arose due to the potential of their use in malicious activities. To address this problem, wework towards the proposed solution by the following twofold contribution, first we propose a novel solution that automates the drone detection and identification processes using drone’s acoustic features with different deep learning algorithms. However, the lack of acoustic drone datasets hinders the ability to implement an effective solution. Therefore, we aim to fulfil this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio clips using a state of the art deep learning model known as the Generative Adversarial Network. Furthermore, we examine the effectiveness of using drone audio with different deep learning algorithms, namely, the Convolutional Neural Network, the Recurrent Neural Network and the Convolutional Recurrent Neural Network in drone detection and identification. Moreover, we investigate the impact our proposed hybrid dataset has on drone detection. The second contribution is laying the foundation for the next step of the anti-drone proposed system which is focused around swarm drones localisation and tracking using data fusion of audio and radio frequency signals using deep learning techniques. This is made possible through the design of a novel swarm of drones simulator. Our findings prove the advantage of using deep learning techniques with acoustic data for drone detection and identification while confirming our hypothesis on the benefits of using the Generative Adversarial Networks to generate real-like drone audio clips with an aim of enhancing the detection of new and unfamiliar drones.

Authors: Sara A. Al-Emadi

MSc in Computing – Thesis

Published on January 2021

You can find the full text here


Cite this paper via the following BibTeX:

@mastersthesis{saraalemadi_thesis,
title={DDI: Drones Detection and Identification using Deep Learning Techniques},
author={AL-EMADI, SARA ABDULRAZAQ},
year={2021} }

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