Linear prediction of atmospheric wave-fronts for tomographic Adaptive Optics systems: modelling and robustness assessment

TL;DR

This paper analytically evaluates the accuracy and robustness of various linear prediction models for atmospheric wave-fronts in tomographic Adaptive Optics, focusing on their tolerance to model errors and turbulence conditions.

Contribution

It extends previous work by analyzing the robustness of linear prediction algorithms under realistic model errors and turbulence conditions.

Findings

+/- 100% wind-speed error is tolerable

+/- 50 degrees error is tolerable

Best predictor outperforms no-prediction within these error bounds

Abstract

We use a theoretical frame-work to analytically assess temporal prediction error functions on von-Karman turbulence when a zonal representation of wave-fronts is assumed. Linear prediction models analysed include auto-regressive of order up to three, bilinear interpolation functions and a minimum mean square error predictor. This is an extension of the authors' previously published work (see ref. 2) 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 behaviour of the previous results under less ideal conditions. Results show that +/- 100pc wind-speed error and +/- 50 deg are tolerable before the best linear predictor delivers poorer performance than the no-prediction case.