Fraanje Rufus, Rice Justin, Verhaegen Michel, Doelman Niek
Delft University of Technology, Delft Center for Systems and Control, Mekelweg 2, 2628 CD Delft, The Netherlands.
J Opt Soc Am A Opt Image Sci Vis. 2010 Nov 1;27(11):A235-45. doi: 10.1364/JOSAA.27.00A235.
Efficient and optimal prediction of frozen flow turbulence using the complete observation history of the wavefront sensor is an important issue in adaptive optics for large ground-based telescopes. At least for the sake of error budgeting and algorithm performance, the evaluation of an accurate estimate of the optimal performance of a particular adaptive optics configuration is important. However, due to the large number of grid points, high sampling rates, and the non-rationality of the turbulence power spectral density, the computational complexity of the optimal predictor is huge. This paper shows how a structure in the frozen flow propagation can be exploited to obtain a state-space innovation model with a particular sparsity structure. This sparsity structure enables one to efficiently compute a structured Kalman filter. By simulation it is shown that the performance can be improved and the computational complexity can be reduced in comparison with auto-regressive predictors of low order.
利用波前传感器的完整观测历史高效且最优地预测冻结流湍流是大型地基望远镜自适应光学中的一个重要问题。至少出于误差预算和算法性能的考虑，评估特定自适应光学配置的最优性能的准确估计非常重要。然而，由于网格点数量众多、采样率高以及湍流功率谱密度的不合理性，最优预测器的计算复杂度巨大。本文展示了如何利用冻结流传播中的一种结构来获得具有特定稀疏结构的状态空间创新模型。这种稀疏结构使人们能够高效地计算结构化卡尔曼滤波器。通过仿真表明，与低阶自回归预测器相比，性能可以得到提高，计算复杂度可以降低。
