This paper presents a customized detection and tracking algorithm for vision-based non cooperative UAS sense and avoid. Obstacle detection and tentative tracking for track confirmation are based on top-hat and bottom-hat morphological filtering, local image analysis for a limited set of regions of interest, and a multi-frame processing in stabilized coordinates. Once firm tracking is achieved, template matching and state estimation based on Kalman filtering are used to track the intruder aircraft and estimate its angular position and velocity. The developed technique has 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”. Performance evaluated in two near collision geometries allows estimating algorithm robustness in terms of sensitivity on weather and illumination conditions, detection range and false alarm rate, and overall tracking accuracy.

Morphological filtering and target tracking for vision-based UAS sense and avoid

FASANO, GIANCARMINE;ACCARDO, DOMENICO;TIRRI, ANNA ELENA;MOCCIA, ANTONIO;De Lellis, Ettore
2014

Abstract

This paper presents a customized detection and tracking algorithm for vision-based non cooperative UAS sense and avoid. Obstacle detection and tentative tracking for track confirmation are based on top-hat and bottom-hat morphological filtering, local image analysis for a limited set of regions of interest, and a multi-frame processing in stabilized coordinates. Once firm tracking is achieved, template matching and state estimation based on Kalman filtering are used to track the intruder aircraft and estimate its angular position and velocity. The developed technique has 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”. Performance evaluated in two near collision geometries allows estimating algorithm robustness in terms of sensitivity on weather and illumination conditions, detection range and false alarm rate, and overall tracking accuracy.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/581837
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