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Optimal wavefront reconstruction from a Shack-Hartmann sensor by use of a Bayesian algorithm

Bakut, P.A., Kirakosyants, V.E., Loginov, V.A., Solomon, C.J., Dainty, J.C. (1994) Optimal wavefront reconstruction from a Shack-Hartmann sensor by use of a Bayesian algorithm. Optics Communications, 109 (1-2). pp. 10-15. ISSN 0030-4018. (doi:10.1016/0030-4018(94)90730-7) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:48558)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://dx.doi.org/10.1016/0030-4018(94)90730-7

Abstract

An optimal algorithm has been derived for the reconstruction of random wavefronts using information on local slopes obtained with Hartmann-type sensors. The approach is based on calculation of the Bayesian posterior mean of the distribution. The accuracy of the method has been investigated via derivation of the error covariance matrix and subsequent calculation of the residual mean square error for waves which have propagated through the turbulent atmosphere. The dependence of the accuracy on the spatial correlation scale of the wavefront, the number of sensor channels and their signal-to-noise ratio is also discussed.

Item Type: Article
DOI/Identification number: 10.1016/0030-4018(94)90730-7
Uncontrolled keywords: Algorithms, Atmospheric turbulence, Calculations, Errors, Estimation, Mathematical models, Matrix algebra, Optical correlation, Polynomials, Sensors, Signal to noise ratio, Statistical optics, Bayesian algorithms, Bayesian posterior mean of distribution, Error covariance matrix, Optimal wavefront reconstruction, Residual mean square error, Sensor channels, Shack-Hartmann sensor, Spatial correlation scale, Wavefronts
Subjects: Q Science
Divisions: Divisions > Division of Natural Sciences > Physics and Astronomy
Depositing User: Giles Tarver
Date Deposited: 18 May 2015 14:04 UTC
Last Modified: 16 Nov 2021 10:19 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/48558 (The current URI for this page, for reference purposes)

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