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-front 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 estimating the location of the centroid of the image that is formed from a guide star within each subaperture. Conventional techniques for centroid estimation involve the use of a linear estimator and conversion tables. Neural networks provide nonlinear solutions to this problem. We address the use of neural networks for estimating the location of the centroid from the subaperture image. We find that neural networks provide more accurate estimates over a larger dynamic range and with less variance than do the conventional linear centroid estimator.