一种基于机器学习和TTAO-VMD算法的双质振动MEMS陀螺仪的新奇温度补偿模型
Wenbo Tan1, Yan Wang2, Xinwang Wang3,4
1School of Software, North University of China, Taiyuan 030051, China.
Micromachines
|January 28, 2026
概括
本研究引入了一种新的温度补偿模型,用于双质振动MEMS陀螺仪 (DMVMG),以提高导航精度. 这种新方法显著减少了因温度变化引起的角度随机步行和偏差不稳定性.
科学领域:
- * 微电子机械系统 (MEMS) 技术
- * 传感器信号处理和补偿
- * 导航系统和惯性测量单元
背景情况:
- *MEMS陀螺仪的输出容易受到温度变化的影响,导致漂移错误和导航精度降低.
- *精确的温度补偿对于提高MEMS陀螺仪在精密导航应用中的性能至关重要.
研究的目的:
- *为双质振动MEMS陀螺仪 (DMVMG) 提出和验证一种新的温度补偿模型.
- *通过减轻温度引起的错误,显著提高DMVMG的导航精度.
主要方法:
- * 开发一个混合补偿模型,集成TTAO-VMD,1D-CNN-Bi-GRU-Attention和SHAKF算法.
- *使用TTAO-VMD进行信号预处理,用于噪声分解和过.
- *使用1D-CNN-Bi-GRU的温度错误建模-注意温度,变化速度,时间和噪声作为输入.
- * 对温度补偿输出进行优化信号组件的重建.
主要成果:
- * 角度随机步行 (N) 显著减少,从18.56°/h降至0.17°/h.
- *偏差不稳定性 (B) 从32.76°/h大幅下降到0.82°/h.
- *使用艾伦方差方法进行实验验证,证实了该模型的有效性.
结论:
- * 拟议的集成TTAO-VMD,1D-CNN-Bi-GRU-Attention和SHAKF模型有效地弥补了DMVMG中的温度漂移.
- * 增强的补偿显著提高了陀螺仪的性能,满足了高精度导航的关键要求.
- * 这项研究为克服MEMS陀螺仪应用中与温度相关的挑战提供了强大的解决方案.
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