Performance investigation of stochastic parallel gradient descent algorithm based wave-front sensor-less adaptive optics for atmosphere turbulence compensation

Abstract

Stochastic parallel gradient descent (SPGD) algorithm is a kind of simple and practicable algorithm for adaptive optics (AO) to compensate the effects of turbulence. The relationship between the number of control variables and convergence effects is explored experimentally in this paper. We find that the number of control variables is not always the larger the better. Under the condition of weak turbulence, the algorithm with 6×6 control variables gives better results than 12×12 control variables and 8×8 control variables. With the increase of turbulence strength, the convergence effects of 12×12 control variables are better than 6×6 control variables and 8×8 control variables. In the condition of weak turbulence, SPGD algorithm with 6×6, 8×8, 12×12 control variables can improve coupling efficiency by 3.2dB, 3.1dB and 3.0dB respectively. In the condition of moderate turbulence, SPGD algorithm with 6×6, 8×8, 12×12 control variables can improve coupling efficiency by 6.4dB, 6.6dB, 7.1dB respectively. In the condition of strong turbulence, SPGD algorithm with 6×6, 8×8, 12×12 control variables can improve coupling efficiency by 6.2dB, 7.1dB, 10.2dB respectively