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Random Error Analysis of MEMS Gyroscope Based on an Improved DAVAR Algorithm.

Jinlong Song1, Zhiyong Shi2, Lvhua Wang3

  • 1Department of Vehicle and Electrical Engineering, Army Engineering University, Shijiazhuang 050003, China. sjzsong_jl@163.com.

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Summary
This summary is machine-generated.

An improved dynamic Allan variance (DAVAR) method enhances signal tracking and estimation confidence by adaptively adjusting window length. This approach overcomes limitations of traditional methods, improving data utilization and stability analysis accuracy.

Keywords:
MEMSdynamic Allan variancekurtosissliding kurtosis contribution coefficient

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Area of Science:

  • Signal Processing
  • Metrology
  • Time Series Analysis

Background:

  • Traditional dynamic Allan variance (DAVAR) methods struggle to balance dynamic tracking with estimation confidence due to fixed window lengths.
  • The use of rectangular windows with fixed lengths limits the adaptability and data utilization in signal analysis.

Purpose of the Study:

  • To propose an improved dynamic Allan variance method that enhances both dynamic tracking capabilities and estimation confidence.
  • To address the limitations of fixed window lengths in traditional DAVAR methods.

Main Methods:

  • Adaptive adjustment of rectangular window length using inverted mirror extension for improved data utilization.
  • Introduction of sliding kurtosis contribution coefficient and kurtosis to sense shock signals and adjust window length.
  • Analysis of window length change factors under different stability conditions based on kurtosis and sliding kurtosis contribution coefficient.

Main Results:

  • The improved DAVAR method demonstrates enhanced dynamic tracking ability when the kurtosis stability threshold approaches 3.
  • Closing the kurtosis stability threshold to 3 improves tracking and estimation confidence in stable intervals without misjudgement.
  • The method effectively balances dynamic tracking and estimation confidence, resolving issues with traditional DAVAR analysis.

Conclusions:

  • The improved DAVAR method successfully overcomes the trade-off between tracking ability and estimation confidence.
  • Adaptive window length adjustment and kurtosis-based stability analysis lead to more accurate signal analysis.
  • This enhanced method provides a more robust solution for analyzing signals with dynamic characteristics.