Multi-sensor data fusion: A tool to enable UAS integration into civil airspace

This paper focuses on data fusion algorithms and flight results from a multi-sensor obstacle detection and tracking system based on radar/electro-optical (EO) fusion and aimed at UAS non-cooperative collision avoidance. The system was developed in the framework of a research project carried out by the Italian Aerospace Research Center and the Department of Aerospace Engineering of the university of Naples "Federico II". It was then installed onboard an optionally piloted flying laboratory of Very Light Aircraft category, and an extensive flight test campaign with a single intruder aircraft was carried out to evaluate the capability of the tracking system to support autonomous collision avoidance. Solutions adopted for the on-board software are discussed regarding obstacle detection in EO images, space/time registration of sensors information, tuning of the tracking filter, handling of sensors' latency, and inclusion of navigation measurements. Potential of radar/EO tracking in terms of achievable accuracy in estimating intruder position and velocity is compared with standalone radar tracking performance. It is demonstrated that the increase in angular accuracy and data rate provided by the EO sensors improves collision detection performance.