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分数阶涡旋光束具有分数轨道角动量(fractional orbital angular momentum, FOAM)模式, 理论上可以无限增加传输容量, 因此在光通信领域具有巨大的应用前景. 然而, 分数阶涡旋光束在自由空间传播时, 螺旋相位的不连续性使其在实际应用中容易受到衍射的影响, 进而影响FOAM阶次识别的准确度, 严重制约基于FOAM的实际应用. 如何实现有衍射条件下的分数阶涡旋光的机器学习识别, 目前仍是一个亟需解决且少见诸报道的问题. 本文提出一种基于残差网络(residual network, ResNet)的深度学习(deep learning, DL)方法, 用于精确识别分数阶涡旋光衍射过程的传播距离和拓扑荷值. 实验结果表明, 该方法可以在湍流条件下识别传播距离为100 cm, 间隔为5 cm, 模式间隔为0.1的FOAM模式, 准确率为99.69%. 该技术有助于推动FOAM模式在测距、光通信、微粒子操作等领域的实际应用.Fractional-order vortex beams possess fractional orbital angular momentum (FOAM) modes, which theoretically have the potential to increase transmission capacity infinitely. Therefore, they have significant application prospects in the fields of measurement, optical communication and microparticle manipulation. However, when fractional-order vortex beams propagate in free space, the discontinuity of the helical phase makes them susceptible to diffraction in practical applications, thereby affecting the accuracy of OAM mode recognition and severely limiting the use of FOAM-based optical communication. Achieving machine learning recognition of fractional-order vortex beams under diffraction conditions is currently an urgent and unreported issue. Based on ResNetA, a deep learning (DL) method of accurately recognizing the propagation distance and topological charge of fractional-order vortex beam diffraction process is proposed in this work. Utilizing both experimentally measured and numerically simulated intensity distributions, a dataset of vortex beam diffraction intensity patterns in atmospheric turbulence environments is created. An improved 101-layer ResNet structure based on transfer learning is employed to achieve accurate and efficient recognition of the FOAM model at different propagation distances. Experimental results show that the proposed method can accurately recognize FOAM modes with a propagation distance of 100 cm, a spacing of 5 cm, and a mode spacing of 0.1 under turbulent conditions, with an accuracy of 99.69%. This method considers the effect of atmospheric turbulence during spatial transmission, allowing the recognition scheme to achieve high accuracy even in special environments. It has the ability to distinguish ultra-fine FOAM modes and propagation distances, which cannot be achieved by traditional methods. This technology can be applied to multidimensional encoding and sensing measurements based on FOAM beam.
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Keywords:
- fractional vortex beams /
- machine learning /
- atmosphere turbulence /
- residual network
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