Segel Max, Gladysz Szymon
Opt Express. 2021 Jan 18;29(2):805-820. doi: 10.1364/OE.408682.
Modal control is an established tool in adaptive optics. It allows not only for the reduction in the controllable degrees of freedom, but also for filtering out unseen modes and optimizing gain on a mode-by-mode basis. When Zernike polynomials are employed as the modal basis for correcting atmospheric turbulence, their cross-correlations translate to correction errors. We propose optimal modal decomposition for gradient-descent-based wavefront sensorless adaptive optics, which is free of this problem. We adopt statistically independent Karhunen-Loève functions for iterative blind correction and analyze performance of the algorithm in static as well as in dynamic simulated turbulence conditions.
模态控制是自适应光学中一种成熟的工具。它不仅允许减少可控自由度，还能滤除不可见模式并逐模式优化增益。当使用泽尼克多项式作为校正大气湍流的模态基时，它们的互相关性会转化为校正误差。我们提出了基于梯度下降的无波前传感器自适应光学的最优模态分解方法，该方法不存在这个问题。我们采用统计独立的卡尔胡宁 - 勒夫函数进行迭代盲校正，并分析了该算法在静态以及动态模拟湍流条件下的性能。
