Drone Detection and Classification using Passive Forward Scattering Radar

• Published in: International Journal of Integrated Engineering
• Main Author: Mamat M.A.C.; Aziz N.H.A.
• Format: Article
• Language: English
• Published: Penerbit UTHM 2022
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132713684&doi=10.30880%2fijie.2022.14.03.010&partnerID=40&md5=3b7a09abbcbfb64fb42cc9955448acab

Radar is a system that can analyze object detection that uses radio waves to determine the range, angle or velocity of the object. The passive radar system consists of both transmitters, to generate microwaves domain and produce the electromagnetic waves for radio system, and the receiver, to receive and process the data obtain from the transmitter signal to determine the Doppler signature of the objects that can be used to detect any presence of drone, aircraft and guided missiles that pass through the system between the transmitter and receiver. The objective of this study was mainly to detect drones, which can be liken to a situation where an unmanned aerial vehicle (UAV) is used, and the drone is mainly used by humans to enter or trespass private and secured zone. Besides that, this study can help improve the security at Malaysian borders or at important events, such as during the latest Malaysian 14th General Election, where man flew a drone during the nomination process. The detection can be done by differentiating the size of the drone and prototype, with a focus on the dimension. In this study, we used passive forward scattering radar for drone detection to get the Doppler signature. The Doppler signature is produced when the antenna detects the presence of the drone passing between the transmitter and receiver. The transmitter produces a power signal that transmits a frequency of Long-Term Evolution (LTE), and in this study, the frequencies used were 1.8 GHz and 2.6 GHz. The 1.8 GHz signal provided better quality compared to 2.6 GHz because it has wider and better network coverage known as 4G LTE as introduced by Maxis provider. Furthermore, all of the data collected was processed and analyzed using MATLAB software to classify drone and prototype signatures through Principal Component Analysis (PCA) results. For future contribution of this project, it can be used at the airport to detect any unwanted drones trespassing the flight departure area, and important areas such as the Federal Administrative Centre of Malaysia, Putrajaya for spying purposes. © Universiti Tun Hussein Onn Malaysia Publisher’s Office