The adaptive coupling of the laser beam from space to single-mode fiber plays an important role in free space optical communication. A typical adaptive algorithm is stochastic parallel gradient descent algorithm (SPGD), which measures the performance index value to estimate the gradient value to maximize the coupling efficiency. It is conceivable that the presence of performance index measurement noise will have a great influence on the convergence performance of the algorithm. We propose an improved Kalman stochastic parallel gradient descent algorithm (KSPGD). Specifically, considering the influence of measurement noise on gradient estimation, we introduce the gradient prediction model in the iterative optimization process and then use the Kalman filter to estimate the gradient of the current iteration point. Kalman filter algorithm and optimization algorithm are integrated together. Simulation and experimental results show that the KSPGD algorithm can better restrain the influence of measurement noise on the convergence performance of the algorithm than the SPGD algorithm.

中文翻译：

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KSPGD算法抑制自适应光纤耦合中测量噪声的影响

激光束从空间到单模光纤的自适应耦合在自由空间光通信中发挥着重要作用。典型的自适应算法是随机并行梯度下降算法（SPGD），它通过测量性能指标值来估计梯度值，以最大化耦合效率。可以想象，性能指标测量噪声的存在会对算法的收敛性能产生很大的影响。我们提出了一种改进的卡尔曼随机并行梯度下降算法（KSPGD）。具体来说，考虑到测量噪声对梯度估计的影响，我们在迭代优化过程中引入梯度预测模型，然后使用卡尔曼滤波器估计当前迭代点的梯度。卡尔曼滤波算法和优化算法集成在一起。仿真和实验结果表明，KSPGD算法比SPGD算法能够更好地抑制测量噪声对算法收敛性能的影响。