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Updated: Dec 29, 2025

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters
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Adaptive filtering for MEMS gyroscope with dynamic noise model.

Yuting Bai1, Xiaoyi Wang1, Xuebo Jin1

  • 1School of Computer and Information Engineering, Beijing Technology and Business University, Beijing, 100048, China; China Light Industry Key Laboratory of Industrial Internet and Big Data, Beijing Technology and Business University, Beijing, 100048, China.

ISA Transactions
|February 9, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive filtering method to reduce noise in Micro-Electro-Mechanical Systems (MEMS) gyroscopes. The novel approach enhances filtering precision for accurate posture recognition and positioning.

Keywords:
Adaptive filteringDynamic Allan varianceKalman filterMEMS gyroscope

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Area of Science:

  • Engineering
  • Instrumentation
  • Signal Processing

Background:

  • Micro-Electro-Mechanical Systems (MEMS) gyroscopes are crucial for posture recognition and positioning systems.
  • Complex noise in MEMS gyroscopes significantly degrades performance, necessitating effective noise filtering techniques.

Purpose of the Study:

  • To develop and validate an adaptive filtering method for improving MEMS gyroscope measurement accuracy.
  • To address the limitations imposed by complex noise on gyroscope performance.

Main Methods:

  • Analysis of MEMS gyroscope noise to establish a dynamic noise model framework.
  • Improvement of dynamic Allan variance using a novel entropy-based truncation window for noise parameter extraction.
  • Derivation of an adaptive Kalman filter based on the developed dynamic noise model.

Main Results:

  • The improved dynamic Allan variance effectively extracts distinct noise features from MEMS gyroscope data.
  • The proposed adaptive filtering method demonstrates relatively high filtering precision.
  • Simulations and experiments validated the efficacy of the noise reduction technique.

Conclusions:

  • The novel adaptive filtering method significantly enhances the precision of MEMS gyroscope measurements.
  • The improved dynamic Allan variance is a key component in accurately characterizing and mitigating gyroscope noise.
  • This research contributes to more reliable posture recognition and assistant positioning systems.