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首页> 外文期刊>Journal of the Optical Society of America, A. Optics, image science, and vision >Generalized aliasing and its implications in modal gain optimization for multi-conjugate adaptive optics
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Generalized aliasing and its implications in modal gain optimization for multi-conjugate adaptive optics

机译：广义混叠及其在多共轭自适应光学器件模态增益优化中的意义

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摘要

The error of generalized aliasing associated with the limited sampling of the atmospheric turbulence volume due to the finite number of wavefront sensing directions in wide-field-of-view adaptive optics is formally defined. Following a modal approach, we extend the direct problem formulation of star-oriented multi-conjugate adaptive optics (MCAO) to model and quantify this error analytically. We show that the turbulence estimation with the least-squares reconstructor is subject to strong generalized aliasing, in particular affecting the badly seen modes, whereas with the minimum-mean-square-error reconstructor the estimation is little affected. Finally, we show that the application of modal gain optimization techniques in closed-loop MCAO systems is jeopardized by the generalized aliasing error.
机译：正式定义了由于宽视场自适应光学器件中有限数量的波前感测方向而导致的与大气湍流体积有限采样有关的广义混叠误差。遵循一种模态方法，我们扩展了面向星形的多共轭自适应光学系统（MCAO）的直接问题公式，以分析和量化该误差。我们表明，使用最小二乘重建器进行湍流估计会受到强烈的广义混叠，尤其是影响到不良模式，而使用最小均方误差重建器时，估计影响很小。最后，我们表明模态增益优化技术在闭环MCAO系统中的应用受到广义混叠误差的危害。

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