Abstract
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.
© 1994 Optical Society of America
Full Article | PDF ArticleMore Like This
Ludovic Meynadier, Vincent Michau, Marie-Thérèse Velluet, Jean-Marc Conan, Laurent M. Mugnier, and Gérard Rousset
Appl. Opt. 38(23) 4967-4979 (1999)
Naoshi Baba, Hiroyuki Tomita, and Noriaki Miura
Appl. Opt. 33(20) 4428-4433 (1994)
Stuart M. Jefferies, Michael Lloyd-Hart, E. Keith Hege, and James Georges
Appl. Opt. 41(11) 2095-2102 (2002)