基于经验正交函数的自适应光学预测控制
Adaptive Optics Predictive Control with Empirical Orthogonal Functions (EOFs)
摘要：
基于波前传感器测量数据的大气波前预报可以大大提高自适应光学系统的性能。我们提出了一个最佳的线性方法的基础上的经验正交函数（ERFF）框架通常用于大气预测。该方法提供了更高的鲁棒性和显着的性能优势，比以前提出的波前预测算法。它可以被实现为线性模式匹配算法，该算法将输入（最近的波前传感器测量）实时分解为先前遇到的模式的线性和，并使用该线性扩展的系数来预测未来状态。该过程对于不断变化的条件、未知的时空相关性和非周期性瞬态事件是鲁棒的，并且使得多个传感器（例如加速度计）能够有助于波前估计。我们通过数值模拟说明了EOFs的优势，并在1 kHz速率系统上证明了1分钟内的滤波器收敛。我们表明，EOFs的方法提供了显着的增益，在高对比度成像，同时减少残留斑点晕和产生的残留斑点晕，是空间和时间上不相关的。
英文摘要：
Atmospheric wavefront prediction based on previous wavefront sensor measurements can greatly enhance the performance of adaptive optics systems. We propose an optimal linear approach based on the Empirical Orthogonal Functions (EOF) framework commonly employed for atmospheric predictions. The approach offers increased robustness and significant performance advantages over previously proposed wavefront prediction algorithms. It can be implemented as a linear pattern matching algorithm, which decomposes in real time the input (most recent wavefront sensor measurements) into a linear sum of previously encountered patterns, and uses the coefficients of this linear expansion to predict the future state. The process is robust against evolving conditions, unknown spatio-temporal correlations and non-periodic transient events, and enables multiple sensors (for example accelerometers) to contribute to the wavefront estimation. We illustrate the EOFs advantages through numerical simulations, and demonstrate filter convergence within 1 minute on a 1 kHz rate system. We show that the EOFs approach provides significant gains in high contrast imaging by simultaneously reducing residual speckle halo and producing a residual speckle halo that is spatially and temporally uncorrelated.
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