Time delay error is a significant error source in adaptive optics (AO) systems. It arises from the latency between sensing the wavefront and applying the correction. Predictive control algorithms reduce the time delay error, providing significant performance gains, especially for high-contrast imaging. However, the predictive controller’s performance depends on factors such as the wavefront sensor (WFS) type, the measurement noise level, the AO system’s geometry, and the atmospheric conditions. We study the limits of prediction under different imaging conditions through spatiotemporal Gaussian process models. The method provides a predictive reconstructor that is optimal in the least-squares sense, conditioned on the fixed times series of WFS data and our knowledge of the atmospheric conditions. We demonstrate that knowledge is power in predictive AO control. With a Shack–Hartmann sensor-based extreme AO instrument, perfect knowledge of the wind and atmospheric profile and exact frozen flow evolution lead to a reduction of the residual wavefront phase variance up to a factor of 3.5 compared with a non-predictive approach. If there is uncertainty in the profile or evolution models, the gain is more modest. Still, assuming that only effective wind speed is available (without direction) led to reductions in variance by a factor of ∼2.3. We also study the value of data for predictive filters by computing the experimental utility for different scenarios to answer questions such as how many past telemetry frames should the prediction filter consider and whether is it always most advantageous to use the most recent data. We show that within the scenarios considered, more data provide a consistent increase in prediction accuracy. Furthermore, we demonstrate that given a computational limitation on how many past frames, we can use an optimized selection of n past frames, which leads to a 10% to 15% additional improvement in root mean square over using the n latest consecutive frames of data.

中文翻译：

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预测的力量：时空高斯过程建模，用于基于斜率的波前传感中的预测控制


时间延迟误差是自适应光学 (AO) 系统中的一个重要误差源。它是由感测波前和应用校正之间的延迟引起的。预测控制算法可减少时间延迟误差，从而显着提高性能，尤其是对于高对比度成像。然而，预测控制器的性能取决于波前传感器 (WFS) 类型、测量噪声水平、AO 系统的几何形状和大气条件等因素。我们通过时空高斯过程模型研究不同成像条件下的预测极限。该方法提供了一种在最小二乘意义上最优的预测重建器，以 WFS 数据的固定时间序列和我们对大气条件的了解为条件。我们证明知识就是预测性 AO 控制的力量。与非预测方法相比，使用基于 Shack-Hartmann 传感器的极端 AO 仪器，对风和大气剖面的完美了解以及精确的冻结流演化可将残余波前相位方差减少达 3.5 倍。如果概况或进化模型存在不确定性，则收益会更小。尽管如此，假设只有有效风速（无方向）会导致方差减少约 2.3 倍。我们还通过计算不同场景的实验效用来研究预测滤波器的数据价值，以回答诸如预测滤波器应考虑多少过去的遥测帧以及使用最新数据是否总是最有利等问题。我们表明，在所考虑的场景中，更多的数据可以持续提高预测准确性。 此外，我们证明，考虑到过去帧数量的计算限制，我们可以使用 n 个过去帧的优化选择，这使得均方根比使用 n 个最新的连续数据帧有 10% 到 15% 的额外改进。