学习从相干光束组合的单个局部模式中估计相位

IF 2.6 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Optical Fiber Technology Pub Date : 2024-11-16 DOI:10.1016/j.yofte.2024.104031

Haoyu Liu , Kun Jin , Jun Li , Jian Wu , Yanxing Ma , Rongtao Su , Jinyong Leng , Pu Zhou

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引用次数: 0

摘要

我们利用轻量级残差神经网络架构（ResNet18），基于非焦平面的单个局部强度模式，为相干光束组合开发了一种先进的主动相位控制方法。通过仅从局部强度图像片段估算相位，可显著提高相机帧频，从而确保更稳健的相位锁定，抵御更大规模的噪声。模拟实验表明，我们的技术能在 19、37 和 61 个通道的范围内为相干光束组合实现有效的相位锁定。

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Learning to estimate phases from single local patterns for coherent beam combination

We developed an advanced active phase control method for coherent beam combining using a lightweight residual neural network architecture (ResNet18), based on single local intensity patterns at non-focal plane. By estimating the phases only from a localized intensity image patch, the camera frame rate can be significantly improved, thereby ensuring more robust phase locking against larger-scale noise. Simulated experiments demonstrated that our technique achieves effective phase locking for coherent beam combination across scales of 19, 37, and 61 channels.

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来源期刊

Optical Fiber Technology 工程技术-电信学

CiteScore

4.80

自引率

11.10%

发文量

327

审稿时长

63 days

期刊介绍： Innovations in optical fiber technology are revolutionizing world communications. Newly developed fiber amplifiers allow for direct transmission of high-speed signals over transcontinental distances without the need for electronic regeneration. Optical fibers find new applications in data processing. The impact of fiber materials, devices, and systems on communications in the coming decades will create an abundance of primary literature and the need for up-to-date reviews. Optical Fiber Technology: Materials, Devices, and Systems is a new cutting-edge journal designed to fill a need in this rapidly evolving field for speedy publication of regular length papers. Both theoretical and experimental papers on fiber materials, devices, and system performance evaluation and measurements are eligible, with emphasis on practical applications.