基于Zernike模式的自适应光学系统随机并行梯度下降算法
Stochastic parallel gradient descent algorithm for adaptive optics system based on Zernike mode
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摘要: 控制算法的收敛速度一定程度上限制了无波前探测自适应光学技术在实时波前畸变校正中的应用。从理论分析角度提出将模式法和区域法结合起来以提高算法收敛速度，并以61单元变形镜为校正器，建立基于随机并行梯度下降算法自适应光学系统仿真模型。结果表明：达到同样的校正效果时，采用组合优化的算法收敛速度要明显优于基于区域法的收敛速度，从而验证了理论分析的合理性。
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关键词:
- 自适应光学系统 /
- 随机并行梯度下降算法 /
- Zernike模式
Abstract: The convergence rate can be the limit of adaptive optics without a wave-front sensor in real-time applications. An improvement on the convergence rate of the stochastic parallel gradient descent(SPGD) algorithm is discussed by combing modal method with zonal method. Based on the SPGD algorithm, adaptive optics systems are simulated with a 61-element deformable mirror. Results show that the modified SPGD algorithm can converge much faster than that based only on zonal method when the same correction effect is obtained. -
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