PREDICTION OF WAVE-FRONT SENSOR SLOPE MEASUREMENTS WITH ARTIFICIAL NEURAL NETWORKS

For adaptive optical systems to compensate for atmospheric turbulence effects, the wave-front perturbation must be measured with a wave-fron t sensor (WFS) and corrected with a deformable mirror. One limitation in this process is the time delay between the measurement of the aberr ated wave front and implementation of the proper correction. Statistic al techniques exist for predicting the atmospheric aberrations at the time of correction based on the present and past measured wave fronts. However, for the statistical techniques to be effective, key paramete rs of the atmosphere and the adaptive optical system must be known. Th ese parameters include the Fried coherence length r(0), the atmospheri c wind-speed profile, and the WFS slope measurement error. Neural netw orks provide nonlinear solutions to adaptive optical problems while of fering the possibility to function under changing seeing conditions wi thout actual knowledge of the current state of the key parameters. We address the use of neural networks for WFS slope measurement predictio n with only the noisy WFS measurements as inputs. Where appropriate, w e compare with classical statistical-based methods to determine if neu ral networks offer true benefits in performance. (C) 1997 Optical Soci ety of America