概括
这项研究引入了一种新的深度神经网络方法,以抑制相位敏感光学时域反射计 (Φ-OTDR) 系统中的干扰色. 多通道数据合成深度神经网络 (MDS-DNN) 改善了无需硬件更改的信号噪声比.
科学领域:
- 光学工程是指光学工程.
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 在相位敏感的光学时域反射计 (Φ-OTDR) 系统中,干扰色会降低传感性能.
- 现有的色抑制方法通常需要对光源进行复杂的硬件修改.
研究的目的:
- 提出一种新的多通道数据合成深度神经网络 (MDS-DNN) 方法,以减少 Φ-OTDR 中的干扰色.
- 为了提高信号噪声比 (SNR) 并降低错误报警率,而不改变传统的 Φ-OTDR 设置.
主要方法:
- 利用 Φ-OTDR 系统中空间过量采样数据中的冗余信息.
- 开发一个基于网络的长期短期记忆 (LSTM) 框架,用于端到端的训练.
- 合成多通道数据以学习与理想传感信号的相关性.
主要成果:
- MDS-DNN算法有效地抑制相位噪声,并在色位置改善SNR.
- 实验结果显示输出SNR为49.88dB,比输入通道增加了19.65dB.
- 该方法将干扰色导致的错误报警率降低了一级.
结论:
- MDS-DNN方法提供了一个有效的解决方案,以减轻 Φ-OTDR 中的干扰色.
- 这种方法可以提高传感性能,而不需要对现有的 Φ-OTDR 硬件进行修改.
- 这种基于深度学习的方法显著改善了SNR,并减少了实际 Φ-OTDR 系统中的错误警报.
相关概念视频
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The LOD indicates the presence or absence...
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Downsampling
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The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...


