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Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
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Generalized aliasing and its implications in modal gain optimization for multi-conjugate adaptive optics.

Fernando Quirós-Pacheco1, Jean-Marc Conan, Cyril Petit

  • 1The Blackett Laboratory, Imperial College London, London SW7 2BW, UK. fquiros@arcetri.astro.it

Journal of the Optical Society of America. A, Optics, Image Science, and Vision
|November 4, 2010
PubMed
Summary

Generalized aliasing error in wide-field-of-view adaptive optics arises from limited atmospheric sampling. Minimum-mean-square-error reconstructors minimize this error, unlike least-squares methods, preserving modal gain optimization in multi-conjugate adaptive optics (MCAO) systems.

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Area of Science:

  • Astronomy and Astrophysics
  • Optical Engineering

Background:

  • Wide-field-of-view adaptive optics systems face challenges with atmospheric turbulence.
  • Limited wavefront sensing directions cause sampling errors in atmospheric turbulence volume.

Purpose of the Study:

  • To formally define and analytically quantify the generalized aliasing error in wide-field-of-view adaptive optics.
  • To investigate the impact of different reconstructors on this error.
  • To assess the effect of this error on modal gain optimization in multi-conjugate adaptive optics (MCAO).

Main Methods:

  • A modal approach was used to extend the direct problem formulation of star-oriented MCAO.
  • Analytical modeling and quantification of the generalized aliasing error.
  • Comparison of least-squares and minimum-mean-square-error reconstructors.

Main Results:

  • Generalized aliasing error is formally defined and analytically quantified.
  • Least-squares reconstructors exhibit strong generalized aliasing, particularly affecting under-sampled modes.
  • Minimum-mean-square-error reconstructors show minimal impact from generalized aliasing.

Conclusions:

  • Generalized aliasing error significantly impacts turbulence estimation in MCAO.
  • The choice of reconstructor is critical for mitigating this error.
  • Modal gain optimization in closed-loop MCAO systems is jeopardized by generalized aliasing.