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

机译:层析成像自适应光学系统大气波前的线性预测:建模和鲁棒性评估

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We use a theoretical framework to analytically assess temporal prediction error functions on von-Karman 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. A 31, 101 (2014)], 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. (C) 2015 Optical Society of America
机译:当假设波前为纬向时,我们使用理论框架来分析评估冯-卡尔曼湍流的时间预测误差函数。分析的线性预测模型包括最多三个阶的自回归,双线性插值函数和最小均方误差预测器。这是作者先前发表的著作Correia等人的扩展。 [J.选择。 Soc。上午。 31,101(2014)],其中建立了各种时间预测模型的功效。在这里,我们检查了这些算法对特定形式的模型错误的容忍度,从而定义了在不太理想的条件下先前结果的预期行为变化。结果表明,在最佳线性预测器提供比非预测情况更差的性能之前,可以容忍+/- 100%的风速误差和+/- 50度。 (C)2015年美国眼镜学会

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