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Framework to trade optimality for local processing in large-scale wavefront reconstruction problems
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DOI：10.1364/OL.41.005162.png)
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Abstract
En 中文
We show that the minimum variance wavefront estimation problems permit localized approximate solutions, in the sense that the wavefront value at a point (excluding unobservable modes, such as the piston mode) can be approximated by a linear combination of the wavefront slope measurements in the point's neighborhood. This enables us to efficiently compute a wavefront estimate by performing a single sparse matrix-vector multiplication. Moreover, our results open the possibility for the development of wavefront estimators that can be easily implemented in a decentralized/distributed manner, and in which the estimate optimality can be easily traded for computational efficiency. We numerically validate our approach on Hudgin wavefront sensor geometries, and the results can be easily generalized to Fried geometries. (C) 2016 Optical Society of America
Keywords:
ADAPTIVE OPTICS SYSTEMS
EXTREMELY LARGE TELESCOPES
DECAY BOUNDS
MATRICES
INVERSE
IDENTIFICATION
DECOMPOSITION
COMPUTATION
EQUATION
Journal
IF:
3.3
Papers:
4.0W
Citations:
7.6W