In various laser applications with multiple beams, measuring the pointing errors of different beams is a prerequisite. Normally, this is done by multiple measurements of corresponding laser spots after separating each beam. In this study, a neural network method is utilized to predict the position and angular errors of dual lasers from a single superimposed spot image on a tilted detector, and thus separates the two laser spots. We find that the key point of decoupling the superimposed image is to enlarge the size difference of the two spots on the detector. Also, optimized intensities of the dual spots to balance their prediction abilities, can help obtain a better prediction performance. This technique provides a novel strategy for advancing pointing measurement in multi-beam optical systems with simplified structure design, better reliability and synchronization.

在具有多光束的各种激光应用中，测量不同光束的指向误差是前提。通常，这是通过在分离每个光束之后对相应的激光点进行多次测量来完成的。在这项研究中，利用神经网络方法从倾斜检测器上的单个叠加点图像中预测双激光的位置和角度误差，从而将两个激光点分开。我们发现去耦叠加图像的关键是扩大检测器上两个光斑的大小差异。同样，优化双点强度以平衡其预测能力，可以帮助获得更好的预测性能。这项技术为简化结构设计，在多光束光学系统中进行指向测量提供了一种新颖的策略，