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Multi-Dimensional Wave Front Sensing Algorithms For Embedded Tracking And Adaptive Optics Applications, Christopher C. Wood
Multi-Dimensional Wave Front Sensing Algorithms For Embedded Tracking And Adaptive Optics Applications, Christopher C. Wood
Theses and Dissertations
Current tracking and adaptive optics techniques cannot compensate for fast-moving extended objects, which is important for ground-based telescopes providing space situational awareness. To fill this need, a vector-projection maximum-likelihood wave-front sensing algorithm development and testing follows for this application. A derivation and simplification of the Cramer-Rao Lower Bound for wavefront sensing using a laser guide star bounds the performance of these systems and guides implementation of a vastly optimized maximum-likelihood search algorithm. A complete analysis of the bias, mean square error, and variance of the algorithm demonstrates exceptional performance of the new sensor. A proof of concept implementation shows feasibility …
Maximum Likelihood Estimation Of Wave Front Slopes Using A Hartmann-Type Sensor, Scott A. Sallberg
Maximum Likelihood Estimation Of Wave Front Slopes Using A Hartmann-Type Sensor, Scott A. Sallberg
Theses and Dissertations
Current methods for estimating the wave front slope at the pupil of a telescope equipped with a Hartmann-type wave front sensor (H-WFS) are based on a simple centroid calculation of the intensity distributions (spots) recorded in each subaperture of the H-WFS. The centroid method does not include any knowledge concerning correlation properties of the slopes over the subapertures or the amount of light collected by the telescope and diverted to the H-WFS for wave front reconstruction purposes. This thesis devises a maximum likelihood (ML) estimation of the spot centroids by incorporating statistical knowledge of the spot shifts. The light level …