Open Access
2 August 2023 Deep learning assisted far-field multi-beam pointing measurement
Xunzheng Li, Chun Peng, Xiaoyan Liang
Author Affiliations +
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.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Xunzheng Li, Chun Peng, and Xiaoyan Liang "Deep learning assisted far-field multi-beam pointing measurement," Optical Engineering 62(8), 086102 (2 August 2023). https://doi.org/10.1117/1.OE.62.8.086102
Received: 15 February 2023; Accepted: 20 July 2023; Published: 2 August 2023
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KEYWORDS
Deep convolutional neural networks

Deep learning

Education and training

Vibration

Amplitude modulation

Cameras

Optical engineering

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