用机器学习提升微机电陀螺仪校准精度，降低噪声并增强稳定性。
MEMS Gyroscope Multi-Feature Calibration Using Machine Learning Technique
- 采用XGBoost和MLP模型，利用多信号融合提升校准能力。
- 相比传统方法，噪声显著降低，测量准确性和稳定性明显提升。
- 适合消费电子与环境监测，高精度场景可选深度学习模型。
陀螺仪在导航、稳定和控制系统中对精确角速度测量至关重要。微机电（MEMS）陀螺仪具有体积小、成本低的优势，但存在复杂且随时间变化的误差与不准确性。本研究利用机器学习技术，通过多信号输入的MEMS谐振式陀螺仪实现更优校准。采用以高预测精度和处理非线性关系能力著称的XGBoost，以及具备多层隐藏结构、可建模复杂模式的MLP模型，均显著降低了噪声，提升了测量准确性和稳定性，优于传统校准方法。尽管深度学习模型计算开销较高，但在高可靠性应用场景中表现优异；而机器学习模型则更适合消费电子和环境监测等对效率要求高的场景。结果表明，先进校准技术可有效提升MEMS陀螺仪性能与校准效率。
原文摘要 · Abstract (English)
Gyroscopes are crucial for accurate angular velocity measurements in navigation, stabilization, and control systems. MEMS gyroscopes offer advantages like compact size and low cost but suffer from errors and inaccuracies that are complex and time varying. This study leverages machine learning (ML) and uses multiple signals of the MEMS resonator gyroscope to improve its calibration. XGBoost, known for its high predictive accuracy and ability to handle complex, non-linear relationships, and MLP, recognized for its capability to model intricate patterns through multiple layers and hidden dimensions, are employed to enhance the calibration process. Our findings show that both XGBoost and MLP models significantly reduce noise and enhance accuracy and stability, outperforming the traditional calibration techniques. Despite higher computational costs, DL models are ideal for high-stakes applications, while ML models are efficient for consumer electronics and environmental monitoring. Both ML and DL models demonstrate the potential of advanced calibration techniques in enhancing MEMS gyroscope performance and calibration efficiency.
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