Small UAVs and in particular the class of micro-UAVs, whose mass is below 2 kg, are constantly rising in popularity for personal as well as professional use, since they are beneficial in many fields such as defense, transportation, monitoring and agriculture. In spite of their advantages, UAVs can be used for terrorist attacks to fly over restricted areas, transport illegal materials or cause an accident in crowded areas. Thus, in the event of non-cooperative drone users, radars can be ideal detection devices, due to their capability to operate day and night in all-weather conditions. However, due to birds and UAVs having similar altitude, speed and radar cross section, many false alarms can occur. For this reason, it is urgent to develop techniques to distinguish UAVs and birds amongst other radar contacts that may be present.

The classification of drones and birds is not unprecedented. Plenty of acoustic, camera and radar solutions are available in the market today. Radar applications frequently rely specifically on the differences in micro-Doppler signature between these two classes. However, limited solutions exist based on the differences in flight behavior, which is referred as "track behavior" in the radar domain. Assuming that drones and birds have different kinematic characteristics, the question arises, whether these can be tracked by surveillance radars in such a way that radar processing can recognize either or both, based on a given tracking time interval. In particular, possible methods include the exploration of the flights of drones and birds, the kinematic features extraction from tracking their trajectories, their implementation to machine learning models in order to classify these two flying objects, as well as the evaluation of their classification accuracy.

The methodology of this thesis project is divided into two classification cases. First, a 6-Degree-of-Freedom quadcopter simulator is used to generate drone trajectories, while the bird data are created from real bird GPS tracks. The second part of the work classifies real bird and drone trajectories tracked by a man-portable scanning surveillance radar. The current research can provide valuable information for the development of classification algorithms for existing radars, and fill the gaps for future efforts to improve the classification of birds and drones.