光时域反射测量中的脉冲宽度限制
Beyond the Limitation of Pulse Width in Optical Time-domain Reflectometry
摘要：
光时域反射计（OTDR）是分布式时域光纤传感技术的基础。通过将脉冲光注入光纤，可以基于光飞行时间获得事件的距离信息。沿光纤长度的最小可分辨事件间隔称为空间分辨率，其由光脉冲宽度决定。通过减小脉冲宽度，可以提高空间分辨率。然而，与此同时，系统的信噪比下降，并且需要更高速度的设备。为了解决这个问题，已经提出了诸如迭代细分、反褶积和神经网络的数据处理方法。但是，它们都有一些缺点，因此没有得到广泛应用。在这里，我们提出并实验证明了基于深度卷积神经网络的OTDR反卷积神经网络。建立了一个简化的OTDR模型，以生成大量的训练数据。通过优化网络结构和训练数据，实现了有效的OTDR反褶积。仿真和实验结果表明，该神经网络可以实现更准确的反褶积比传统的反褶积算法具有更高的信噪比。
英文摘要：
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.
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