Comparing methods of UAV detection and tracking based on low-cost 3D LiDAR

The increasing diversity of UAV applications requires the ability to detect and track them accurately. We address the interest of a 3D LiDAR network to better cover urban canyons. Detection and tracking of objects using a low-cost 3D LiDAR have primarily been developed for on-vehicle sensors in the context of autonomous vehicles and ADAS with processing choices particularly suited for ground objects detection. We compare trackingby- detection strategies based on object classification through supervised learning and tracking-before-detection strategies that perform tracking without object classification. We discuss the need to favour the latter approach in a defence context for identifying protean threats. We conduct flight tests with a drone to gather real data from a low-cost 3D LiDAR, as well as ground-truth drone position data, to create a test database for UAV detection and tracking methods. The test scenario includes variations in the drone’s altitude, distance to the sensor, and flight speed, in order to evaluate the tracking methods under realistic conditions and to test the limits of the low-cost 3D LiDAR sensor. Based on these data, we implement two processing methods for multi-target UAV tracking. We evaluate the performance of these approaches in terms of estimation quality. In a broader sense, we assess the applicability of classical approaches developed for autonomous vehicles and ground object detection and tracking using on-vehicle 3D LiDAR sensors, in an anti-drone defence context in urban areas. This assessment focuses on detecting and tracking protean UAV based on degraded 3D LiDAR measurements. We identify innovation avenues to improve or surpass these approaches for this purpose.