Flight Performance Assessment of Vision-based Detection and Tracking for UAS Sense and Avoid

This paper focuses on a vision-based detection and tracking algorithm for UAS non cooperative collision avoidance. It is based on top-hat and bottom-hat morphological filtering, local image analysis for a limited set of regions of interest, and a multi-frame tracking algorithm in stabilized coordinates that is used to confirm intruder detection. Once this detection is achieved, template matching-based algorithms are used to track the intruder in subsequent frames. The developed technique has been tested using flight data gathered in the framework of the sense and avoid research project carried out by the Italian Aerospace Research Center and the Department of Industrial Engineering of the university of Naples “Federico II”. First results are promising: considering two frontal encounters in completely different illumination conditions, range for confirmed detection of an ultra-light intruder aircraft is in both cases of the order of 2.5 km, no false tracks are generated, and angular measurement accuracy in North-East-Down coordinates is of the order of 0.1°. Analysis of flight images shows that potential sources of false detections, such as small dim clouds and sun glares, are correctly removed.