Challenges and solutions for vision-based sense and avoid

This paper focuses on sensing algorithms for vision-based non cooperative sense and avoid. Obstacle detection and tracking is based first on morphological filtering and local image analysis (detection), then on multi-frame processing in stabilized coordinates (tentative tracking), and finally on template matching and Kalman filtering-based state estimation (firm tracking). A conflict detection logic is introduced which uses an adaptive line-of-sight rate threshold based on the functional dependencies of the distance at closest point of approach in near collision conditions. The derived threshold takes into account ownship motion and estimated intruder azimuth, while assumptions are made regarding detection performance of the electro-optical system and intruder speed. The developed techniques have been tested using flight data gathered in a 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”. Achieved experimental results are promising and are discussed focusing on algorithm tuning and system performance in terms of probability of intruder declaration as a function of range, false alarm rate, tracking accuracy, and reliability of vision-based conflict detection.