Detection and tracking of Near-Earth Objects using a cognitive hierarchical data-association model

Current efforts aimed at detecting and identifying Near-Earth Objects (NEOs) that pose potential risks to Earth use moderate-size telescopes combined with image processing algorithms to detect the motion of these objects. The search strategies of such systems involve multiple revisits at given intervals between observations to the same area of the sky so that objects that appear to move between the observations can be identified against the static star field. The algorithm described in this paper, referred to as Dynamic Logic (DL), has been applied previously to radar signal processing to achieve a track-before-detect capability. This suggests that DL could improve the detection of extremely dim moving objects in image data as well. The concept of hierarchical dynamic logic is used to supervise image pre-processing and interpret and detect moving objects directly from star-field. The proposed method shows a promising ability to distinguish true asteroid tracks from false alarms with almost no operator interaction, making it potentially suitable for the task of automatic detection of NEOs.