Drone detection, recognition, and assistance system for counter-UAV with VIS, radar, and radio sensors

Abstract

Current capabilities and sales volume of present-day UAVs (unmanned aerial vehicles) strongly demand counterUAV systems in a lot of applications to protect facilities or areas from misused or threatening drones. In order to reach a maximum detection and information gathering performance such systems need to combine different detection subsystems, i.e. based on visual optical, radar, and radio sensors. But available systems on the market are very expensive, the price is typically far over half a million dollars. Therefore, a far more cost-efficient solution has been developed which is presented in this paper. Four high-resolution visual optical cameras offer full 360 degree observation at distances up to several hundred meters. As soon as UAVs are visible in an image as small dots, they are detected and tracked with a GPU-based point target detector. Radar and radio sensor subsystems detect UAVs at higher distances. A full HD camera on a pan and tilt unit successively focuses on each found object to enable a convolutional neural network (CNN) to classify it with a higher local image resolution to identify UAVs and discard false alarms, e.g. from birds. Furthermore, drone type and payload are determined with CNNs, too, and a laser rangefinder on the pan and tilt unit measures the object distance. All information is collected and visualized in a 2D or 3D environmental map or situation representation on the base of geo-coordinates that are computed based on a RTK GNSS sensor self-localization. All software and hardware components are described in detail. The overall system is powerful, modular, scalable, and cost-efficient.