UAV Classification

UAV classification research focuses on reliably identifying and tracking unmanned aerial vehicles (UAVs) using diverse sensor data, including visual, LiDAR, radar, and radio frequency signals. Current efforts concentrate on developing robust multi-modal classification pipelines that fuse information from multiple sensors, often employing clustering algorithms like DBSCAN for point cloud processing and advanced deep learning techniques for feature extraction and classification. These advancements are crucial for improving UAV security, enabling effective airspace management, and supporting applications ranging from national security to autonomous systems.