Maximum a priori estimation of wavefront slopes using a Hartmann wavefront sensor

Computer Science – Performance

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Scientific paper

Current methods for estimating the wavefront slope at the pupil of a telescope using a Hartmann wavefront sensor (H- WFS) are based on a simple centroid calculation of the irradiance distributions (spots) recorded in each subaperture. The centroid calculation does not utilize knowledge concerning the correlation properties of the slopes over the subapertures or the amount of light collected by the H-WFS. This paper presents the derivation of a maximum a priori (MAP) estimation of the irradiance centroids by incorporating statistical knowledge of the wavefront tilts. Information concerning the light level in each subaperture and the relative spot size is also employed by the estimator. The MAP centroid estimator is found to be unbiased and the mean squared error performance is upper bounded by that exhibited by the classical centroid technique. This error performance is demonstrated using Kolmogorov wavefront slope statistics for various light levels.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Maximum a priori estimation of wavefront slopes using a Hartmann wavefront sensor does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Maximum a priori estimation of wavefront slopes using a Hartmann wavefront sensor, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Maximum a priori estimation of wavefront slopes using a Hartmann wavefront sensor will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-1143247

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.