Analysis On Drone Detection and Classification in LTE-Based Passive Forward Scattering Radar System

• Published in: International Journal of Integrated Engineering
• Main Author: Aziz N.H.A.; Fodzi M.H.M.; Shariff K.K.M.; Haron M.A.
• Format: Article
• Language: English
• Published: Penerbit UTHM 2023
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170259833&doi=10.30880%2fijie.2023.15.03.011&partnerID=40&md5=5b97a7a1401c6b3b03a45e5c7ad70fe1

Long-Term Evolution (LTE) is most commonly used in connection with 4G networks with high spectral efficiency, high peak data rates, flexible in frequency and bandwidth. By utilizing LTE signal in passive forward scattering radar as transmitter, this system is able to create a microwave domain at the radar's receiver part which generated a moving object's Doppler signature. The emergence of guided missiles, humans, airplanes, and drones that travel through between the forward scatter radar systems can really be spotted with this passive radar system. This study's primary goal is to employ passive forward scattering radar and an LTE signal to detect drones, which are commonly used by individuals to violate or invade private and secure places. In detail, a drone was detected at two distinct heights of two meters (lower) and three meters (higher) from the ground by utilizing passive forward scattering radar to generate Doppler signature of the flying drone. This experimental work is conducted at two locations which are Taman Suria (UiTM, Shah Alam) and Teluk Kemang (Port Dickson), due to the telecommunication transmitter antenna transmits Long-Term Evolution (LTE) signal with frequency of 1.8 GHz and 2.6 GHz. The results of drone detection at various heights were evaluated using Principal Component Analysis (PCA) on all the experimental data obtained. According to the evaluation, the lower height of the drone performed better in classification and confusion matrices analysis than the upper height due to a larger cross-sectional area for the lower height of the drone that travelled through the forward scatter zone. In summary, the overall study clearly demonstrates the effective categorization of flying drone detection at upper and lower positions in Principle Component Analysis (PCA). For future contribution of this research, 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