Efficient Identification of UAVs Through Automatic Communication Frame Linking

Unmanned Aerial Vehicles (UAVs) are increasingly utilized across both professional and private sectors. However, their ability to transmit sensitive information poses potential threats to public safety. Current detection techniques often monitor radio frequencies to identify drones and ascertain their models. Despite this, they are unable to differentiate between two UAVs of the same model. This paper aims to address this limitation by identifying individual UAVs of the same model. We conducted a measurement campaign to capture RF communication signals from two DJI MAVIC 2 Zoom drones. We characterized the communication frames, timing, intervals, power, and shape through a detailed statistical analysis of these signals. This analysis revealed distinguishing characteristics that enable the identification and tracking of individual UAV communications, even when multiple UAVs of the same model are present. Based on these findings, we developed a system that automatically links communication frames to specific UAVs, allowing for accurate counting of UAVs in a given area.