Noise reduction and key atmospheric parameter estimation using artificial neural networks

Details Noise reduction and key atmospheric parameter estimation using artificial neural networks Noise reduction and key atmospheric parameter estimation using artificial neural networks

Aug 1995

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1995spie.2534..357m&link_type=abstract

Proc. SPIE Vol. 2534, p. 357-370, Adaptive Optical Systems and Applications, Robert K. Tyson; Robert Q. Fugate; Eds.

Statistics

Applications

Scientific paper

Current interests include the use of adaptive optics systems to image celestial and earth orbiting objects from the ground. Methods, such as the minimum variance reconstructor, have been developed to improve the imaging performance of adaptive optics systems. However, one tool available which has not been fully investigated is the artificial neural network. Neural networks provide nonlinear solutions to adaptive optics problems while offering the possibility to adapt to changing seeing conditions. In this paper we address the use of neural networks for three tasks: (1) to reduce the wave front sensor (WFS) noise variance, (2) to estimate the Fried coherence length, r(subscript o), and (3) to estimate the variance of the WFS noise. All of these tasks are accomplished using only the noisy WFS measurements as input. Where appropriate, we compare to classical statistics based methods to determine if neural networks offer true benefits in performance. We find that neural networks perform well in all three tasks. While a statistics based method is found to perform better than a neural network in reducing WFS noise variance, neural networks perform better than the statistics based methods in estimating r(subscript o) and the variance of the WFS noise.

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