UAV detection, tracking, and classification by sensor fusion of a 360° lidar system and an alignable classification sensor

The number of reported incidents caused by UAVs, intentional as well as accidental, is rising. To avoid such incidents in future, it is essential to be able to detect UAVs. However, not every UAV is a potential threat and therefore the UAV not only has to be detected, but classified or identified. 360o scanning LiDAR systems can be deployed for the detection and tracking of (micro) UAVs in ranges up to 50 m. Unfortunately, the verification and classification of the detected objects is not possible in most cases, due to the low resolution of that kind of sensor. In this paper, we propose an automatic alignment of an additional sensor (mounted on a pan-tilt head) for the identification of the detected objects. The classification sensor is directed by the tracking results of the panoramic LiDAR sensor. If the alignable sensor is an RGB- or infrared camera, the identification of the objects can be done by state-of-the-art image processing algorithms. If a higher-resolution LiDAR sensor is used for this task, algorithms have to be developed and implemented. For example, the classification could be realized by a 3D model matching method. After the handoff of the object position from the 360o LiDAR to the verification sensor, this second system can be used for a further tracking of the object, e.g., if the trajectory of the UAV leaves the field of view of the primary LiDAR system. The paper shows first results of this multi-sensor classification approach.