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
Original Manuscript: September 27, 1993
Revised Manuscript: March 29, 1994
Published: September 20, 1994
Richard L. Kendrick, D. S. Acton, and A. L. Duncan, "Phase-diversity wave-front sensor for imaging systems," Appl. Opt. 33, 6533-6546 (1994)