Machine learning based accurate recognition of fractional optical vortex modes in atmospheric environment
Machine learning based accurate recognition of fractional optical vortex modes in atmospheric environment
复制标题
基于机器学习的大气环境分数光学涡旋模式的精确识别
DOI：
10.1063/5.0061365
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发表时间：
2021-10-04
影响因子：
4
通讯作者：
Yin, Jianping
中科院分区：
文献类型：
--
作者：
Cao, Meng;Yin, Yaling;Yin, Jianping
具有分数轨道角动量的光涡旋光束在提高经典和量子体制下的光通信和信息处理能力方面具有很大的潜力。然而，自由空间的大气湍流使涡旋光束的螺旋相前发生畸变，引起模式扩散，严重阻碍了涡旋光束的实际应用。本文采用改进残差神经网络结构的卷积神经网络方法，克服了对AT中分数阶OAM进行准确识别的障碍。正如花瓣干涉图所示，利用一种携带双OAM模式的混合光束提供两个可控自由度，以便更好地识别更细微的OAM模式，例如分数拓扑电荷数l和角比n。我们的研究表明，当l和n不同时，在强AT参数（Cn2 = 5 × 10−14 m−2/3）和较长的传播距离（z = 1500 m）下，OAM对2万幅图像的识别准确率高达85.30%。我们的研究结果代表了在大气环境中高精度识别具有宽带宽的分数OAM的显着成就，扩展了基于机器学习的OAM光通信的普遍应用。
Optical vortex beam with fractional orbital angular momentum (OAM) has great potential to increase the capacity of optical communication and information processing in classical and quantum regimes. However, atmospheric turbulence (AT) in free space distorts the helical phase-front of vortex beams and causes the mode diffusion, seriously hindering the practical application. Herein, using a convolutional neural network approach with an improved residual neural network architecture, we overcome the hurdle to give the accurate recognition of the fractional OAM in the AT. As demonstrated on the petal interference patterns, a type of hybrid beams carrying double OAM modes is utilized to provide two controllable degrees of freedom for greater recognition of more subtle OAM modes, e.g., the fractional topological charge number l and the angular ratio n. Our studies show that with various l and n, the recognition accuracy of OAM over 20 000 images is as high as 85.30% even under the strong AT parameter ( Cn2 = 5 × 10−14 m−2/3) and the long propagation distance (z = 1500 m). Our findings represent a remarkable achievement toward highly accurate recognition of fractional OAM with broad bandwidth in the atmospheric environment, expanding the applications for the general interest of machine learning based OAM optical communication.