Using AKF-PSR to Compensate Random Drift Errors of Low-Cost MEMS Gyroscopes

The random drift of a micro-electromechanical system (MEMS) gyroscope seriously affects its measurement accuracy. To model and compensate its random drift, the time series analysis method has widely been deployed, which, however, requires a large amount of data for pre-processing analysis and is uns...

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Bibliographic Details
Published in:IEEE sensors journal 2019-08, Vol.19 (16), p.6802-6810
Main Authors: Xiao, Chunsheng, Zhu, Qingyuan, Hu, Huosheng, Zhang, Xiaoyu, Chen, Xuanwei, Song, Xinyi
Format: Article
Language:English
Subjects:
Adaptive filters
adaptive Kalman filter (AKF)
Drift
Gyroscopes
Indexes
Kalman filters
Low cost
MEMS gyroscopes
Microelectromechanical systems
Micromechanical devices
Model accuracy
phase space reconstruction (PSR)
Predictive models
random drift errors
Sensors
Time series
Time series analysis
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Summary:The random drift of a micro-electromechanical system (MEMS) gyroscope seriously affects its measurement accuracy. To model and compensate its random drift, the time series analysis method has widely been deployed, which, however, requires a large amount of data for pre-processing analysis and is unsuitable for real-time applications. This paper proposes a new random drift compensation method based on the adaptive Kalman filter (AKF) and phase space reconstruction (PSR). AKF is first designed to compensate the random drift of the low-cost MEMS gyroscope. The phase variables are then used as phase vectors via PSR. Experiments show that the proposed AKF-PSR method can effectively compensate the random drift of the gyroscope, and the standard deviation is reduced by half.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2019.2912400