采用焦平面传感和卡尔曼滤波的可重构光学系统自动对准
Automated alignment of a reconfigurable optical system using focal-plane sensing and Kalman filtering
摘要：
对准任务的自动化可以提供改进的效率并且极大地增加光学系统的灵活性。具有自动对准能力的当前光学系统通常被设计成包括专用波前传感器。在这里，我们展示了一个可重构系统，只使用焦平面图像的自对准方法。我们定义了一个具有八个自由度的双透镜光学系统。使用ZEMAX软件在给定未对准参数的情况下模拟图像。我们对模拟数据集进行主成分分析（PCA），以获得Karhunen-Loève（KL）模式，这些模式构成了基集，其权重是系统测量值。使用非线性最小二乘拟合来学习将状态映射到测量的模型函数，并且该模型函数用作用于计算控制输入以对准系统的非线性估计器（扩展卡尔曼滤波器和无迹卡尔曼滤波器）的测量函数。我们提出并讨论了整个系统在操作中的模拟和实验结果。
英文摘要：
Automation of alignment tasks can provide improved efficiency and greatly increase the flexibility of an optical system. Current optical systems with automated alignment capabilities are typically designed to include a dedicated wavefront sensor. Here, we demonstrate a self-aligning method for a reconfigurable system using only focal plane images. We define a two lens optical system with eight degrees of freedom. Images are simulated given misalignment parameters using ZEMAX software. We perform a principal component analysis (PCA) on the simulated dataset to obtain Karhunen-Loève (KL) modes, which form the basis set whose weights are the system measurements. A model function which maps the state to the measurement is learned using nonlinear least squares fitting and serves as the measurement function for the nonlinear estimator (Extended and Unscented Kalman filters) used to calculate control inputs to align the system. We present and discuss both simulated and experimental results of the full system in operation.
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