A MEMS IMU Gyroscope Calibration Method Based on Deep Learning

被引：41

作者：

Huang, Fengrong ^{[1
]}

Wang, Zhen ^{[1
]}

Xing, Luran ^{[1
]}

Gao, Chunyan ^{[1
]}

机构：

[1] Hebei Univ Technol, Sch Mech Engn, Tianjin 300400, Peoples R China

来源：

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT | 2022年 / 71卷

基金：

中国国家自然科学基金;

关键词：

Micromechanical devices; Gyroscopes; Calibration; Deep learning; Inertial navigation; Error compensation; Data models; deep learning; gyroscope; inertial navigation;

D O I：

10.1109/TIM.2022.3160538

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The errors of microelectromechanical system (MEMS) inertial measurement units (IMUs) are huge, complex, nonlinear, and time varying. The traditional calibration method based on a linear model for calibration and compensation is obviously not applicable. In this article, a calibration method based on deep learning is proposed for MEMS IMU gyroscopes. In this method, the output model of MEMS IMU gyroscope is constructed by using the temporal convolutional network. Based on the powerful data processing capability of deep learning, the error features are obtained from the gyroscope data in the past, and the gyroscope data after the error compensation can be regressed. The method in this article is validated on public dataset. The experimental results show that compared with the existing MEMS sensor error compensation method based on deep learning, the attitude and position accuracy obtained by the inertial navigation solution using the compensated gyroscope data are improved, which proves that the proposed method can effectively and accurately calibrate the gyroscope error.

引用

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