5G Radar and Wi-Fi Based Machine Learning on Drone Detection and Localization

Ignatius Teo, M., Seow, C. K. and Wen, K. (2021) 5G Radar and Wi-Fi Based Machine Learning on Drone Detection and Localization. In: 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS), Chengdu, China, 23-26 April 2021, ISBN 9781665412568 (doi: 10.1109/ICCCS52626.2021.9449224)

Abstract

Drone usages have been proliferating for various government initiatives, commercial benefits and civilian leisure purposes. Drone mismanagement especially civilian usage drones can easily expose threat and vulnerability of the Government Public Key Infrastructures (PKI) that hold crucial operations, affecting the survival and economic of the country. As such, detection and location identification of these drones are crucial immediately prior to their payload action. Existing drone detection solutions are bulky, expensive and hard to setup in real time. With the advent of 5G and Internet of Things (IoT), this paper proposes a cost effective bistatic radar solution that leverages on 5G cellular spectrum to detect the presence and localize the drone. Coupled with K-Nearest Neighbours (KNN) Machine Learning (ML) algorithm, the features of Non- Line of Sight (NLOS) transmissions by 5G radar and Received Signal Strength Indicator (RSSI) emitted by drone are used to predict the location of the drone. The proposed 5G radar solution can detect the presence of a drone in both outdoor and indoor environment with accuracy of 100%. Furthermore, it can localize the drone with an accuracy of up to 75%. These results have shown that a cost effective radar machine learning system, operating on the 5G cellular network spectrum can be developed to successfully identify and locate a drone in real-time.

• Item Type: Conference Proceedings
• Keywords: Drone, 5G radar, line of sight, non-line of sight, machine learning.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Seow, Dr Chee Kiat
• Authors: Ignatius Teo, M., Seow, C. K., and Wen, K.
• Subjects: Q Science > QA Mathematics; T Technology > T Technology (General); T Technology > TA Engineering (General). Civil engineering (General)
• College/School: College of Science and Engineering > School of Computing Science
• Research Centre: College of Science and Engineering > School of Computing Science > IDA Section
• ISBN: 9781665412568
• Published Online: 21 June 2021
• Copyright Holders: Copyright © 2021 IEEE
• First Published: First published in Proceedings of 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS)
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher