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Proceedings Paper

Atmospheric modeling with the intent of training a neural net wavefront sensor
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Paper Abstract

Modeling atmospheric turbulence which plays a critical role in the training of neural network wavefront sensors is discussed in the framework of an adaptive optics program for the multiple mirror telescope. It is concluded that the accuracy of the wavefront correction possible with a neural network directly depends on the similarity of the training images to those seen in the telescope. The image simulations used in the training of neural network wavefront sensors are based on a random mid-point displacement (RMD) algorithm and sine wave summation algorithms. The RMD algorithm is considered to be an extremely fast method of wavefront generation used for very large arrays and image sequences without time evolution. Multiple turbulent layer atmospheric models based on the sine wave summation algorithm create image sequences with temporal structure functions that closely match real structure function data.

Paper Details

Date Published: 1 August 1992
PDF: 9 pages
Proc. SPIE 1688, Atmospheric Propagation and Remote Sensing, (1 August 1992); doi: 10.1117/12.137920
Show Author Affiliations
D'nardo Colucci, Optical Sciences Ctr./Univ. of Arizona (United States)
Michael Lloyd-Hart, Steward Observatory/Univ. of Arizona (United States)
Peter L. Wizinowich, Steward Observatory/Univ. of Arizona (United States)
James Roger P. Angel, Steward Observatory/Univ. of Arizona (United States)


Published in SPIE Proceedings Vol. 1688:
Atmospheric Propagation and Remote Sensing
Anton Kohnle; Walter B. Miller, Editor(s)

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