Bulk wind estimation and prediction for adaptive optics control systems

We present a wind-predictive controller for astronomical adaptive optics (AO) systems that is able to predict the motion of a single windblown layer in the presence of other, more slowly varying phase aberrations. This controller relies on fast, gradient-based optical flow estimation to identify the velocity of the translating layer and a recursive mean estimator to account for turbulence that varies on a time scale much slower than the operating speed of the AO loop. We derive the Cramer-Rao lower bound for the wind estimation problem and show that the proposed estimator is very close to achieving theoretical minimum-variance performance. We also present simulations using on-sky data that show significant Strehl increases from using this controller in realistic atmospheric conditions.