USE OF ARTIFICIAL NEURAL NETWORKS FOR HARTMANN-SENSOR LENSLET CENTROID ESTIMATION

For adaptive optical systems to compensate for atmospheric-turbulence effects, the wave-front perturbation must be measured with a wave-fron t sensor (WFS). A Hartmann WFS typically divides the optical aperture into subapertures and then measures the slope of the wave front within each subaperture. Hartmann WFS slope measurements are based on estima ting the location of the centroid of the image that is formed from a g uide star within each subaperture. Conventional techniques for centroi d estimation involve the use of a Linear estimator and conversion tabl es. Neural networks provide nonlinear solutions to this problem. We ad dress the use of neural networks for estimating the location of the ce ntroid fi om the subaperture image. We find that neural networks provi de more accurate estimates over a larger dynamic range and with less v ariance than do the conventional linear centroid estimator.