论文标题
超出光学时间域反射测定法中脉冲宽度的限制
Beyond the Limitation of Pulse Width in Optical Time-domain Reflectometry
论文作者
论文摘要
光学时域反射仪（OTDR）是分布式时间域光纤传感技术的基础。通过将脉冲光注射到光纤中，可以根据光飞行时间获得事件的距离信息。沿纤维长度的最小可区分事件分离称为空间分辨率，该分辨率由光脉冲宽度确定。通过减小脉冲宽度，可以改善空间分辨率。但是，与此同时，系统的信噪比会降低，并且需要更高的速度设备。为了解决此问题，已经提出了数据处理方法，例如迭代细分，反卷积和神经网络。但是，它们都有一些缺点，因此没有被广泛应用。在这里，我们提出并在实验上证明了基于深卷积神经网络的OTDR反卷积神经网络。构建了简化的OTDR模型，以生成大量的培训数据。通过优化网络结构和培训数据，可以实现有效的OTDR反卷积。模拟和实验结果表明，所提出的神经网络可以比传统的反向卷积算法获得更准确的反卷积，具有较高的信噪比。
Optical time-domain reflectometry (OTDR) is the basis for distributed time-domain optical fiber sensing techniques. By injecting pulse light into an optical fiber, the distance information of an event can be obtained based on the time of light flight. The minimum distinguishable event separation along the fiber length is called the spatial resolution, which is determined by the optical pulse width. By reducing the pulse width, the spatial resolution can be improved. However, at the same time, the signal-to-noise ratio of the system is degraded, and higher speed equipment is required. To solve this problem, data processing methods such as iterative subdivision, deconvolution, and neural networks have been proposed. However, they all have some shortcomings and thus have not been widely applied. Here, we propose and experimentally demonstrate an OTDR deconvolution neural network based on deep convolutional neural networks. A simplified OTDR model is built to generate a large amount of training data. By optimizing the network structure and training data, an effective OTDR deconvolution is achieved. The simulation and experimental results show that the proposed neural network can achieve more accurate deconvolution than the conventional deconvolution algorithm with a higher signal-to-noise ratio.
