Global ETD Search

1	Rogue Drone Detection Raheem, Muiz Olalekan January 2023 (has links) Rogue drones have become a significant concern in recent years due to their potential to cause harm to people and property and disrupt critical infrastructure and public safety. As a result, there has been a growing need for effective methods to detect and mitigate the risks posed by these drones. The proposed study aims to address the task by using a Radio Frequency (RF) based approach. Also, ensemble Machine Learning (ML) methods, as well as Deep Learning (DL) techniques were utilized as classification algorithms. Three levels of classification were defined for the task which includes drone detection, identification, and characterization based on operation mode. For the three levels, Deep-Complex Convolutional Neural Network performed the best and achieved an average accuracy of 99.82%, 94.20%, and 90.25%, respectively. Rogue Drones Machine Learning Deep Learning Radio Frequency Communication Systems Kommunikationssystem Computer Systems Datorsystem Signal Processing Signalbehandling Telecommunications Telekommunikation