Kendrick R L, Acton D S, Duncan A L
Appl Opt. 1994 Sep 20;33(27):6533-46. doi: 10.1364/AO.33.006533.
A phase-diversity wave-front sensor has been developed and tested at the Lockheed Palo Alto Research Labs (LPARL). The sensor consists of two CCD-array focal planes that record the best-focus image of an adaptive imaging system and an image that is defocused. This information is used to generate an object-independent function that is the input to a LPARL-developed neural network algorithm known as the General Regression Neural Network (GRNN). The GRNN algorithm calculates the wave-front errors that are present in the adaptive optics system. A control algorithm uses the calculated values to correct the errors in the optical system. Simulation studies and closed-loop experimental results are presented.
一种相位分集波前传感器已在洛克希德·帕洛阿尔托研究实验室（LPARL）开发并进行了测试。该传感器由两个电荷耦合器件（CCD）阵列焦平面组成，用于记录自适应成像系统的最佳聚焦图像和离焦图像。这些信息用于生成一个与物体无关的函数，该函数作为LPARL开发的一种称为广义回归神经网络（GRNN）的神经网络算法的输入。GRNN算法计算自适应光学系统中存在的波前误差。一种控制算法利用计算值来校正光学系统中的误差。文中给出了模拟研究和闭环实验结果。
