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Related Concept Videos

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A gyroscope is defined as a spinning disk in which the axis of rotation is free to assume any orientation. When spinning, the orientation of the spin axis is unaffected by the orientation of the body that encloses it. The body or vehicle enclosing the gyroscope can be moved from place to place, while the orientation of the spin axis remains the same. This makes gyroscopes very useful in navigation, especially where magnetic compasses cannot be used, such as in crewed and crewless spacecraft,...
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Consider a coffee mug hanging on a hook in a pantry. If the mug gets knocked, it oscillates back and forth like a pendulum until the oscillations die out.
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Related Experiment Video

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Magnetic Tweezers for the Measurement of Twist and Torque
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An Integrated MEMS Gyroscope Array with Higher Accuracy Output.

Honglong Chang1, Liang Xue2, Wei Qin2

  • 1Micro and Nano Electro Mechanical System Laboratory, Northwestern Polytechnical University, Xi'an City, Shaanxi Province, P. R. China, 710072. changhl@nwpu.edu.cn.

Sensors (Basel, Switzerland)
|November 24, 2016
PubMed
Summary

This study introduces a two-level Kalman filtering method for Micro-Electro-Mechanical Systems (MEMS) gyroscope arrays. The integrated system significantly enhances gyroscope accuracy by optimally estimating and compensating for noise sources.

Keywords:
MEMS gyroscopesaccuracy improvinggyroscope array.optimal filteringrandom noise

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Area of Science:

  • Engineering
  • Sensor Technology
  • Signal Processing

Background:

  • MEMS gyroscopes are crucial for navigation and motion sensing.
  • Individual MEMS gyroscopes suffer from noise and bias drift, limiting their accuracy.
  • High-accuracy applications often require expensive, specialized inertial sensors.

Purpose of the Study:

  • To develop an integrated MEMS gyroscope array method to improve sensor accuracy.
  • To enhance the performance of low-cost MEMS gyroscopes for high-accuracy applications.
  • To reduce bias drift and noise in MEMS gyroscope measurements.

Main Methods:

  • Designed a two-level optimal filtering approach using Kalman filtering.
  • Implemented a first-level filter combining identical gyroscopes to create a virtual sensor.
  • Developed a second-level integrated Kalman filter utilizing external sensors (magnetometers, accelerometers) for attitude determination.

Main Results:

  • The first-level filter reduced bias drift from 35°/hr to 1.07°/hr for three gyroscopes.
  • The second-level filter further decreased bias drift to 0.53°/hr.
  • Demonstrated significant accuracy improvement compared to individual MEMS gyroscopes.

Conclusions:

  • The proposed integrated MEMS gyroscope array effectively enhances gyroscope accuracy.
  • This method enables the use of low-cost MEMS sensors in applications demanding high precision.
  • The optimal filtering strategy successfully mitigates noise and bias drift in MEMS gyroscopes.