Stochastic parallel gradient descent algorithm for adaptive optics system

Huimin, 马慧敏 Ma; Pengfei, 张鹏飞 Zhang; Jinghui, 张京会 Zhang; Chengyu, 范承玉 Fan; Yingjian, 王英俭 Wang

doi:10.3788/hplpb20102206.1206

Cited by 3 publications

(1 citation statement)

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“…To evaluate the performance of the SPGD algorithm, we presented the evolution curves of the performance metric. When the perturbations amplitude is fixed, the convergence rate is determined by the gain coefficient to a great extent [9] [13] . To improve SPGD convergence, in the simulation we adopt an adaptive adjusting gain regime in which the control parameter is adjusted to the varied value of the metric, γ=J/C, where C is a negative constant.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

Tip-tilt adaptive correction based on stochastic parallel gradient descent optimization algorithm

Zhang

et al. 2010

SPIE Proceedings

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Adaptive optics correcting technique based on stochastic parallel gradient descent (SPGD) control algorithm is an alternative approach which is independent of wavefront sensor and optimizes the performance metric directly. In this paper we establish a simulation model of tip-tilt adaptive optics system, where SPGD optimization algorithm is used to correct the tip-tilt aberration induced by dynamic turbulence. The distance between the measured centroid of a blurring image and the demarcated centroid of the ideal image is considered as the system performance metric, and signals applied to compensation of the drifting atmosphere are generated for tip-tilt mirror. The sensitivity of the performance metrics is investigated, and the convergence rate and the stability are analyzed. By means of this optimization algorithm, the centroid of the image is modified in real time, which results in the improvement of the long exposure image.

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Section: Simulation Results and Analysismentioning

confidence: 99%