Unmanned Aerial Vehicle Classification Using Neural Neworks and Radar Digital Twins: UAV Classification Using Neural Networks and Radar Digital Twins

Unmanned aerial vehicles (UAVs), commonly known as drones, have emerged as versatile tools with a wide range of applications across various fields. UAV technology has rapidly evolved, leading to its adoption in civilian and commercial sectors. UAVs offer numerous benefits, including cost-effectiveness, flexibility, and accessibility, making them invaluable assets in various industries such as precision agriculture, environmental monitoring, disaster response, and infrastructure inspection. They provide real-time aerial data and imagery, enabling farmers to optimize crop management, conservationists to monitor ecosystems, and emergency responders to assess disaster-affected areas [1]. Additionally, UAVs play a crucial role in contactless vital sign monitoring [2] and in entertainment and filmmaking, revolutionizing aerial cinematography and photography [3]. With their versatility and accessibility, UAVs continue to drive innovation and efficiency in industries worldwide. However, the increased accessibility and availability of UAVs have also raised concerns about their potential use in illegal activities and terrorist attacks [4], [5], [6], [7]. This highlights the importance of identifying and classifying drones for safety and security purposes.