Structured light enhanced machine learning for fiber bend sensing

Abstract

The intricate optical distortions that occur when light interacts with complex media, such as few- or multi-mode optical fiber, often appear random in origin and are a fundamental source of error for communication and sensing systems. We propose the use of orbital angular momentum (OAM) feature extraction to mitigate phase-noise and allow for the use of intermodal-coupling as an effective tool for fiber sensing. OAM feature extraction is achieved by passive all-optical OAM demultiplexing, and we demonstrate fiber bend tracking with 94.1% accuracy. Conversely, an accuracy of only 14% was achieved for determining the same bend positions when using a convolutional-neural-network trained with intensity measurements of the output of the fiber. Further, OAM feature extraction used 120 times less information for training compared to intensity image based measurements. This work indicates that structured light enhanced machine learning could be used in a wide range of future sensing technologies.