Opt Express. 2021 Oct 25;29(22):35474-35489. doi: 10.1364/OE.439215.
The general neural networks (NNs) based on classification convert the Brillouin frequency shift (BFS) extraction in Brillouin-based distributed sensing to a problem in which the possible BFS output of the sensing system belongs to a finite number of discrete values. In this paper, we demonstrate a method of applying NNs with adaptive BFS incremental steps to signal processing for Brillouin optical correlation-domain sensing and achieve higher accuracy and operation speed. The comparison with the conventional curving fitting method shows that the NN improves the BFS measurement accuracy by 2-3 times and accelerates the signal processing speed by 1000 times for simulated signals. The experimental results demonstrate the NN provides 1.6-2.7 times enhancement for BFS measurement accuracy and 5000 times acceleration for the BFS extraction speed. This method supplies a potential solution to online signal processing for real-time Brillouin sensing.
基于分类的通用神经网络（NNs）将布里渊频移（BFS）提取转换为布里渊分布式传感系统的可能 BFS 输出属于有限数量的离散值的问题。在本文中，我们展示了一种将具有自适应 BFS 增量步长的 NNs 应用于布里渊光相关域传感信号处理的方法，以实现更高的精度和更快的运算速度。与传统的曲线拟合方法的比较表明，对于模拟信号，NN 提高了 BFS 测量精度 2-3 倍，并将信号处理速度提高了 1000 倍。实验结果表明，NN 为 BFS 测量精度提供了 1.6-2.7 倍的增强，BFS 提取速度提高了 5000 倍。该方法为实时布里渊传感的在线信号处理提供了一种潜在的解决方案。
