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A Novelty Temperature Compensation Model for Dual-Mass Vibration MEMS Gyroscope Based on Machine Learning and

Wenbo Tan1, Yan Wang2, Xinwang Wang3,4

  • 1School of Software, North University of China, Taiyuan 030051, China.

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Summary
This summary is machine-generated.

This study introduces a new temperature compensation model for dual-mass vibration MEMS gyroscopes (DMVMG) to improve navigation precision. The novel method significantly reduces angle random walk and bias instability caused by temperature variations.

Keywords:
MEMS gyroscopeTTAO-VMDdual-mass vibrationmachine learningtemperature compensation

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Area of Science:

  • * MEMS (Micro-Electro-Mechanical Systems) technology
  • * Sensor signal processing and compensation
  • * Navigation systems and inertial measurement units

Background:

  • * MEMS gyroscope outputs are susceptible to temperature variations, leading to drift errors and reduced navigation accuracy.
  • * Accurate temperature compensation is crucial for enhancing the performance of MEMS gyroscopes in precision navigation applications.

Purpose of the Study:

  • * To propose and validate a novel temperature compensation model for dual-mass vibration MEMS gyroscopes (DMVMG).
  • * To significantly improve the navigation precision of DMVMGs by mitigating temperature-induced errors.

Main Methods:

  • * Development of a hybrid compensation model integrating TTAO-VMD, 1D-CNN-Bi-GRU-Attention, and SHAKF algorithms.
  • * Signal preprocessing using TTAO-VMD for noise decomposition and filtering.
  • * Temperature error modeling using 1D-CNN-Bi-GRU-Attention with temperature, rate of change, time, and noise as inputs.
  • * Reconstruction of optimized signal components for temperature-compensated output.

Main Results:

  • * Significant reduction in angle random walk (N) from 18.56 °/h to 0.17 °/h.
  • * Substantial decrease in bias instability (B) from 32.76 °/h to 0.82 °/h.
  • * Experimental validation using the Allan variance method confirmed the model's effectiveness.

Conclusions:

  • * The proposed integrated TTAO-VMD, 1D-CNN-Bi-GRU-Attention, and SHAKF model effectively compensates for temperature drift in DMVMGs.
  • * The enhanced compensation significantly improves gyroscope performance, meeting critical requirements for high-precision navigation.
  • * This research offers a robust solution for overcoming temperature-related challenges in MEMS gyroscope applications.