Noise reduction and key atmospheric parameter estimation using artificial neural networks

Dennis A. Montera; Byron M. Welsh; Michael C. Roggemann; Dennis W. Ruck

doi:10.1117/12.217753

25 August 1995 Noise reduction and key atmospheric parameter estimation using artificial neural networks

Dennis A. Montera, Byron M. Welsh, Michael C. Roggemann, Dennis W. Ruck

Proceedings Volume 2534, Adaptive Optical Systems and Applications; (1995) https://doi.org/10.1117/12.217753
Event: SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, 1995, San Diego, CA, United States

Abstract

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_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_o and the variance of the WFS noise.

Citation Download Citation

Dennis A. Montera, Byron M. Welsh, Michael C. Roggemann, and Dennis W. Ruck "Noise reduction and key atmospheric parameter estimation using artificial neural networks", Proc. SPIE 2534, Adaptive Optical Systems and Applications, (25 August 1995); https://doi.org/10.1117/12.217753

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