Show simple item record

AuthorAl-Sa'd, Mohammad F.
AuthorAl-Ali, Abdulla
AuthorMohamed, Amr
AuthorKhattab, Tamer
AuthorErbad, Aiman
Available date2024-08-14T06:12:18Z
Publication Date2019
Publication NameFuture Generation Computer Systems
ResourceScopus
ISSN0167739X
URIhttp://dx.doi.org/10.1016/j.future.2019.05.007
URIhttp://hdl.handle.net/10576/57695
AbstractThe omnipresence of unmanned aerial vehicles, or drones, among civilians can lead to technical, security, and public safety issues that need to be addressed, regulated and prevented. Security agencies are in continuous search for technologies and intelligent systems that are capable of detecting drones. Unfortunately, breakthroughs in relevant technologies are hindered by the lack of open source databases for drone's Radio Frequency (RF) signals, which are remotely sensed and stored to enable developing the most effective way for detecting and identifying these drones. This paper presents a stepping stone initiative towards the goal of building a database for the RF signals of various drones under different flight modes. We systematically collect, analyze, and record raw RF signals of different drones under different flight modes such as: off, on and connected, hovering, flying, and video recording. In addition, we design intelligent algorithms to detect and identify intruding drones using the developed RF database. Three deep neural networks (DNN) are used to detect the presence of a drone, the presence of a drone and its type, and lastly, the presence of a drone, its type, and flight mode. Performance of each DNN is validated through a 10-fold cross-validation process and evaluated using various metrics. Classification results show a general decline in performance when increasing the number of classes. Averaged accuracy has decreased from 99.7% for the first DNN (2-classes), to 84.5% for the second DNN (4-classes), and lastly, to 46.8% for the third DNN (10-classes). Nevertheless, results of the designed methods confirm the feasibility of the developed drone RF database to be used for detection and identification. The developed drone RF database along with our implementations are made publicly available for students and researchers alike.
SponsorThis publication was supported by Qatar university Internal Grant No. QUCP-CENG-2018/2019-1 . The work of Aiman Erbad is supported by grant number NPRP 7-1469-1-273 . The findings achieved herein are solely the responsibility of the authors.
Languageen
PublisherElsevier
SubjectDeep learning
Drone identification
Machine learning
Neural networks
UAV detection
TitleRF-based drone detection and identification using deep learning approaches: An initiative towards a large open source drone database
TypeArticle
Pagination86-97
Volume Number100
dc.accessType Open Access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record