Deep learning assisted far-field multi-beam pointing measurement
Deep learning assisted far-field multi-beam pointing measurement
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DOI：
10.1117/1.oe.62.8.086102
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发表时间：
2023-08
影响因子：
1.3
通讯作者：
Xunzheng Li;Chun Peng;Xiaoyan Liang
中科院分区：
文献类型：
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作者：
Xunzheng Li;Chun Peng;Xiaoyan Liang
抽象的。我们提出并通过实验验证了一种深度学习方法来同步测量相干光束组合系统的多光束指向误差。该方法仅使用一个探测器即可获取远场干涉焦斑，可以大大降低高精度相干合束系统的复杂度。利用幅度调制来消除对称系统中标签值的混乱。利用位置辅助相机获取准确的标签值，解决了长期数据采集中因环境振动造成的样本与标签值不匹配的问题。仿真和实验中，均方根精度分别约为0.3和0.5μrad，可以极大地满足相干合束系统中的指向测量要求。结果表明，该方法可以很好地应用于高功率激光系统的多光束相干合成。
Abstract. We present and experimentally verify a deep learning approach to synchronously measure the multi-beam pointing error for coherent beam combining systems. This approach uses only one detector by acquiring the far-field interference focal spot, which can greatly reduce the complexity in coherent beam combining systems with high accuracy. The amplitude modulation is utilized to eliminate the confusion of the label values in symmetric system. The position assist camera is used to acquire accurate label value, which solves the mismatch between sample and label value caused by ambient vibration in long-term data acquisition. In simulation and experiment, the RMS accuracy is about 0.3 and 0.5 μrad, respectively, which can greatly meet the pointing measurement requirement in coherent beam combining systems. The result shows that this approach can be well applied to multi-beam coherent combination for high-power laser systems.