Data Fusion for UAS Collision Avoidance: Results from Flight Testing

In the framework of a research project carried out by the Italian Aerospace Research Center (CIRA) and the Department of Aerospace Engineering of the university of Naples “Federico II”, an integrated radar/electro-optical (EO) system configuration was adopted to demonstrate in flight autonomous non-cooperative UAS collision avoidance. Proper image processing and data fusion algorithms were developed to gain full advantage from these heterogeneous sources. The hardware/software prototypical sensing system was 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. This paper focuses on data fusion results from flight tests. Potential of radar/EO tracking is pointed out in terms of achievable accuracy in estimating intruder position and velocity. Analysis of estimated distance at closest point of approach shows how the increase in angular accuracy and data rate provided by the EO sensors improves system reliability in collision risk estimation.