基于随机并行梯度下降(SPGD)算法的自适应光学(AO)无需波前传感器就可以实现对波前相位畸变的闭环补偿。但是算法的收敛速度较慢,制约着其在实时性要求高的系统中的应用。根据SPGD算法中随机扰动对收敛速度的影响,提出一种利用泽尼克(Zernike)模式优化算法随机扰动的方法。针对大气湍流,建立了一个基于SPGD算法的61单元自适应光学仿真模型,并对一组符合Kolmogorov特性的相位畸变进行了数值仿真,分析了该优化方法对系统收敛特性的影响。仿真结果表明,与优化前相比,优化随机扰动后,校正精度略微下降,但是收敛速度得到明显的改善。

Adaptive optics (AO) based on stochastic parallel gradient descent (SPGD) algorithm can be used to correct the phase aberration without the wavefront sensor,but the low speed of convergence confines its application in real-time system. Considering the relationship between the stochastic perturbation and convergence speed of SPGD algorithm,a technique is proposed to optimize the proportion of stochastic perturbation with Zernike mode. Aiming at atmospheric turbulence,a 61-element AO model based on SPGD algorithm is set up,and a group of phase aberration with the Kolmogorov spectrum is simulated numerically to research the effect on convergence characteristic resulting from this method. Results show that,comparing with the AO before optimization,the convergence speed can be improved efficiently although it gives up a little correction precision.