Numerical Processing of Wavefront Aberration Measurements

The estimation of wavefront aberrations is central to both optical testing and real time control of active optical systems. Optical measurements, whether wavefront tilts, optical path differences, or image plane intensities, must be numerically transformed into meaningful aberrations. This dissertation develops new, more accurate and more noise tolerant algorithms to process these measurements. The text begins by presenting a number of generic estimation techniques suitable for processing measurements taken at a single point in time. All of these static processing techniques prove to be variations on the Bayesian minimum variance estimator. Reviews of a point spread function measurement processor and a Hartman sensor processor serve as an introduction to practical aberration measurement processing. Two approaches to testing afocal telescopes with an array of small flats instead of the usual single large flat, called subaperture testing, are analyzed in more detail. Then a new subaperture testing processor is developed which utilizes information on the correlations among subaperture misalignments. Subsequent coding and testing with computer simulated measurements show that the new processor is significantly more accurate than classical algorithms. The static aberration measurement theory is then extended to accommodate sequences of measurements taken over a number of points in time. A new subaperture testing measurement processor using a Kalman filter is developed in order to take advantage of the dynamic nature of these measurements. Coding and testing with computer simulated measurements for a number of test cases with known aberrations, show that the new processor is considerably more accurate than any previous method.