WCSE

Compensation of MEMS Gyroscope Random Error Based on Square- Root Risk-Sensitive Unscented Kalman Filter

Abstract— In order to compensate MEMS gyroscope random error, a new method employing the squareroot risk-sensitive unscented Kalman filter (SR-RSUKF) and a nonlinear model is proposed. The nonlinear model based on ARIMA takes model parameters as states, and thus realizes the online model estimation. The SR-RSUKF deals with non-additive noise items through augmented state vector, and employs a square root algorithm to get well numerical stability, and improves the flexibility by extending scalar risk parameters to a risk sensitive matrix. In experiments, the raw sample data is processed with three methods using different models and filters. And the results show that the SR-RSUKF together with the nonlinear model provide a competent solution to compensate MEMS gyroscope random error.

Index Terms— MEMS gyroscope, random error, Unscented Kalman Filter

Shi Gang, Li Xisheng, Wang Zhe, Kang Ruiqing, Shu Xiongying
School of Automation and Electrical Engineering, University of Science and Technology Beijing, CHINA
Shi Gang
College of Information and Control Engineering, China University of Petroleum, CHINA
Shengli college China University of Petroleum, CHINA
Li Xisheng
Beijing Engineering Research Center of Industrial Spectrum Imaging, CHINA
Li Ruibin
School of Electronic and Information Engineering, Beijing University of Aeronautics and Astronautics, CHINA

ISBN: 978-981-11-3671-9 DOI: 10.18178/wcse.2017.06.17Xsrc="http://www.wcse.org/uploadfile/2019/0823/20190823055609629.png" style="width: 120px; height: 68px;" />[Download]

Cite: Shi Gang, Li Xisheng, Li Ruibin, Wang Zhe, Kang Ruiqing, Shu Xiongying, "Compensation of MEMS Gyroscope Random Error Based on Square- Root Risk-Sensitive Unscented Kalman Filter," Proceedings of 2017 the 7th International Workshop on Computer Science and Engineering, pp. 1169-1173, Beijing, 25-27 June, 2017.