A Markov-Random-Field-based approach to modeling and prediction of atmospheric turbulence

Nowadays, the adaptive optics (AO) system is of fundamental importance to improve the real resolution of ground-based telescopes. In practical applications the telescope resolution is limited by the atmospheric turbulence. The aim of the AO system is that of estimating the atmospheric turbulence and computing a suitable input for a set of deformable mirrors to reduce the turbulence effect. A commonly accepted assumption is that of considering the turbulence as formed by a discrete set of layers moving over the telescope lens. In this paper, we first propose a method for estimating the number of layers and their characteristics. Then, we exploit the information on the turbulence layers to construct a linear predictor of the turbulent phase. Performance of the proposed method is shown by means of simulations.