Optimizing Free-Space Optical Communication Based Distributed Temperature Sensing Demodulation Using Meta-Learning

Making the best use of expensive distributed temperature sensing (DTS) systems and saving costs is helpful for their measurement applications. The integration of free space optics (FSO) and DTS systems helps to reduce the cost of laying fiber optic cables and maintaining optical cables. In addition, machine learning is a common means to optimize the measurement demodulation of distributed fiber sensing. This work uses meta-learning to demodulate the measurement results of DTS. Meta-learning allows the use of small amounts of data to work, thereby reducing the data modeling cost and time of actual measurement applications. The atmospheric turbulence caused by the introduction of FSO or the attenuation or scattering caused by long-distance optical fibers may introduce noise into the temperature distribution curve measured by DTS. Therefore, this work also simulates the temperature distribution curve with noise and applies the discrete wavelet transform (DWT) to remove noise to reduce the data quality degradation caused by noise. Results show that the proposed meta-learning model can adapt faster and significantly improve the temperature event detection performance. Therefore, the proposed FSO and meta-learning system are cost-effective for applications in DTS and can enhance the distributed fiber optic sensing system.