Improved Near Earth Orbiting Asteroid Detection via Statistical Image Fusion

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 asteroid motion. These algorithms detect objects via assumptions about the point-like nature of the target. This assumption breaks down in poor seeing conditions when the object no longer resembles a point source. This paper serves to document an alternative approach involving the use of many smaller apertures whose images are fused using Bayesian decision techniques that assume nothing about the shape of the target in order to determine the presence of a NEO. The technique is shown to be robust in the presence of atmospheric turbulence. Simulation studies are conducted showing the feasibility of the proposed technique.