Linear prediction of atmospheric wave-fronts for tomographic adaptive optics systems: modelling and robustness...

We use a theoretical framework to analytically assess temporal prediction error functions on von-Kármán turbulence when a zonal representation of wavefronts is assumed. The linear prediction models analyzed include auto-regressive of an order up to three, bilinear interpolation functions, and a minimum mean square error predictor. This is an extension of the authors' previously published work Correia et al. [J. Opt. Soc. Am. A31, 101 (2014)JOAOD61084-752910.1364/JOSAA.31.000101], in which the efficacy of various temporal prediction models was established. Here we examine the tolerance of these algorithms to specific forms of model errors, thus defining the expected change in behavior of the previous results under less ideal conditions. Results show that ±100% wind speed error and ±50  deg are tolerable before the best linear predictor delivers poorer performance than the no-prediction case.