为了实现基于双AWG的FBG阵列传感器与LSTM神经网络的连续和准确的解调
Optics express
|November 11, 2025
概括
本研究引入了一种新的算法,用于使用长短期内存 (LSTM) 神经网络和双阵列波导网格 (AWG) 的光纤布拉格格 (FBG) 阵列,用于智能传感应用中的精确,连续的波长解调.
科学领域:
- 光子学和光学传感器
- 在工程领域的人工智能.
- 信号处理 信号处理
背景情况:
- 纤维布拉格格 (FBG) 阵列对于传感至关重要,但在高密度解调方面面临挑战.
- 传统方法在复杂的光谱数据中难以准确和特征表示.
- 为了增强FBG传感,需要同步的多通道光谱采集.
研究的目的:
- 为FBG阵列提出一个具有超高精度的连续解调算法.
- 利用长短期记忆 (LSTM) 神经网络和双阵列波导网格 (AWG) 来改进光谱获取和特征提取.
- 为高密度FBG阵列应用提供强大的技术解决方案.
主要方法:
- 使用双阵列波导网格 (AWG) 进行交叉,同步的多通道光谱采集.
- 采用长期短期记忆 (LSTM) 神经网络来处理多维光谱数据以进行波长预测.
- 开发了一个端到端的算法,集成特征提取和波长预测.
主要成果:
- 实现了超高的解调精度和增强的特征表示.
- 通过实验验证证明了通过实验验证的连续高精度解调.
- 在温度监测中,达到平均最大预测误差为±12.125 pm,平均平均误差为±1.672 pm.
结论:
- 提出的基于LSTM和双AWG的算法为高密度FBG阵列的高效解调提供了可靠的解决方案.
- 这种方法结合了高精度和稳定性,推进了智能传感能力.
- 为精确的光谱转移检测和分析提供了一条新的技术途径.
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