The growing number of Unmanned Aerial Vehicles (UAVs) in both military and civilian sectors necessitates advanced tracking and detection systems. This paper presents the development and field trial results of a high-resolution radar and optical system for UAV detection conducted within the NATO SPS project “Anti-Drones”. By using a multichannel linear frequency modulated continuous wave (FMCW) radar architecture, the system demonstrates robust capabilities in identifying and tracking various UAV types, including “mini” and “micro” drones. Field trials evaluated detection accuracy, tracking reliability, and payload recognition. Advanced signal processing and machine learning (ML) algorithms enhanced real-time recognition and reduced false alarms. Integrating radar data with optical camera tracking further improved performance in multi-target scenarios. This study highlights the system’s potential for military and civilian applications in addressing UAV threats. Future research will focus on refining data fusion algorithms and extending operational range based on trial insights.