Use of AI to Diversify and Improve the Performance of RF Sensors Drone Detection Mechanism
Fahad Alsifiany, King Fahad Security College, Saudi Arabia
Abstract
Drone terrorism may seem elementary and efforts in its mitigation may seem painless. The fact is that security bodies in many countries are still grappling with this growing security concern. The autonomous nature of drones and the unpredictable nature of drone attacks remain to be some of the unforeseen challenges undermining the mitigation efforts in combating drone terrorism. The need to upskill our security forces and the general public on the operational practices and security capabilities in the drone world cannot be overemphasized. This paper explores a futuristic solution to the current challenges encountered in the war against drone terrorism. In its design, it delves into the possibility of utilizing Artificial Intelligence (AI) in characterizing the features of drones identified in our airspace to determine their authenticity. It further enriches the employees of the security services and the general public with information on combating drone terrorism by benefiting from the accumulated experiences of the relevant and specialized affiliates.
Keywords
Drone terrorism, Drone, Radio Frequency (RF), Unmanned Aerial Vehicles (UAV), Artificial Intelligence (AI), Computer Vision (CV), Machine Learning (ML).
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#sensors #wireless ireless #droneterrorism #drone #radiofrequency #uav #artificialintelligence #computervision #machinelearning
