Improved near-Earth object detection using Dynamic Logic

Current efforts aimed at detecting and identifying Near Earth Objects (NEOs) that pose potential risks to Earth use moderately sized 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. Dynamic Logic algorithm, derived from Modeling Field Theory, has made significant improvements in detection, tracking, and fusion of ground radar images. As an extension to this, the research in this paper will examine Dynamic Logic's ability to detect NEOs with minimal human-in-the-loop intervention. Although the research in this paper uses asteroids for the automation detection, the ultimate extension to this study is for detecting orbital debris. Many asteroid orbits are well defined, so they will serve as excellent test cases for our new algorithm application.