Experimental Demonstration of Stochastic Parallel Gradient Descent Control Algorithm for Adaptive Optics System

Huizhen, 杨慧珍 Yang; Bo, 陈波 Chen; Xinyang, 李新阳 Li; Wenhan, 姜文汉 Jiang

doi:10.3788/aos20082802.0205

Cited by 8 publications

(3 citation statements)

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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%

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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%

“…This adaptive optics correction system without wavefront sensor involves simple components and costs lowly. As so far, a large number of research results show that the stochastic parallel gradient descent algorithm is the most efficient stochastic optimization algorithm [6][7][8][9] .…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

et al. 2010

SPIE Proceedings

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“…Entropy evaluation function and statistics function are extremely sensitive to additive constants such as the background levels, their applications are therefore limited. Simulated Annealing (SA) [2], Genetic Algorithm(GA) [3], Simplex Algorithm [4] and Stochastic Parallel Gradient Descent(SPGD) [5] [6] are the common stochastic parallel search algorithms. In Comparison with SPGD, the convergence accuracy of SA is relatively low, GA needs to adjust large numbers of control parameters, and the simplex algorithm is inclined to falling into local extremes.…”

Section: Introductionmentioning

confidence: 99%

High resolution imaging of phase-distorted extended object using SPGD algorithm and deformable mirror

Yang

Jiang

2007

SPIE Proceedings

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The conventional adaptive optics (AO) technique based on phase conjugate principle requires the information about a reference wave front that corresponds to the undistorted image when it is applied to extended source image correction. The AO technique based on the image function appears more promising than the conventional AO for this application. The success of this technique depends on an appropriate image quality criterion and a suitable control algorithm. We tried to find a convenient image performance metric not from additional optics elements, but only from the distorted image which is a gray level image. Simulation results showed the variance function of gray level presents more potential than other ways, such as ways based on the information about the gray level gradient of each pixel. Based on SPGD algorithm, an AO system was simulated with a 61-element deformable mirror and the above resultant image quality criterion. The correction capability of the AO system was investigated through different images and different level aberrations. Numerical simulation results verified the performance metric we used is effective. The system can correct different level disturbed images successfully when parameters of the algorithm were appropriate for static distorted wave-fronts。

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Phase calibration method of transducer arrays considering acoustic interactions

Chen

2020

2020 IEEE 2nd International Conference on Civil Aviation Safety and Information Technology (ICCASIT

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Experimental Demonstration of Stochastic Parallel Gradient Descent Control Algorithm for Adaptive Optics System

Cited by 8 publications

References 0 publications

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

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

High resolution imaging of phase-distorted extended object using SPGD algorithm and deformable mirror

Phase calibration method of transducer arrays considering acoustic interactions

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