用机器学习提升光纤光栅解调器的精度与泛化能力
Machine Learning-Driven Compensation for Non-Ideal Channels in AWG-Based FBG Interrogator
- 用机器学习回归替代传统解析模型进行校准
- 误差降低至3.17皮米，跨2.9纳米波段保持亚5皮米精度
- 无需重新校准，适合多类型传感器部署
本文实验研究了一种基于氮化硅（SiON）集成阵列波导光栅（AWG）的光纤布喇格光栅（FBG）解调器。尽管基于AWG的解调器具有紧凑和可扩展的优点，但其实际性能受非理想光谱响应限制。为此，在2.4纳米波长范围内比较了两种校准策略：(1) 基于西格玛函数拟合的分段解析模型；(2) 基于机器学习的回归模型。解析方法在标定范围内实现7.11皮米的均方根误差（RMSE），而基于指数回归的机器学习方法达到3.17皮米。此外，该机器学习模型在扩展至2.9纳米波长范围时仍保持亚5皮米精度，无需重新拟合。残差与误差分布分析揭示了两种方法的权衡。机器学习校准提供了一种鲁棒、数据驱动的替代方案，显著提升非理想信道响应下的精度，减少人工校准工作量，并增强对多种FBG传感器配置的可扩展性。
原文摘要 · Abstract (English)
We present an experimental study of a fiber Bragg grating (FBG) interrogator based on a silicon oxynitride (SiON) photonic integrated arrayed waveguide grating (AWG). While AWG-based interrogators are compact and scalable, their practical performance is limited by non-ideal spectral responses. To address this, two calibration strategies within a 2.4 nm spectral region were compared: (1) a segmented analytical model based on a sigmoid fitting function, and (2) a machine learning (ML)-based regression model. The analytical method achieves a root mean square error (RMSE) of 7.11 pm within the calibrated range, while the ML approach based on exponential regression achieves 3.17 pm. Moreover, the ML model demonstrates generalization across an extended 2.9 nm wavelength span, maintaining sub-5 pm accuracy without re-fitting. Residual and error distribution analyses further illustrate the trade-offs between the two approaches. ML-based calibration provides a robust, data-driven alternative to analytical methods, delivering enhanced accuracy for non-ideal channel responses, reduced manual calibration effort, and improved scalability across diverse FBG sensor configurations.
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