Precision improving solutions based on ARMA model and modified self-adapted Kalman filter for MEMS gyro

Xiao-yu Jiang; Yan-tao Zong; Xi Wang; Zhuo Chen; Zhong-xuan Liu

doi:10.1117/12.871790

9 November 2010 Precision improving solutions based on ARMA model and modified self-adapted Kalman filter for MEMS gyro

Xiao-yu Jiang, Yan-tao Zong, Xi Wang, Zhuo Chen, Zhong-xuan Liu

Proceedings Volume 7853, Advanced Sensor Systems and Applications IV; 78533W (2010) https://doi.org/10.1117/12.871790
Event: Photonics Asia 2010, 2010, Beijing, China

Abstract

MEMS gyro is used in inertial measuring fields more and more widely, but random drift is considered as an important error restricting the precision of it. Establishing the proper models closed to actual state of movement and random drift, and designing a kind of effective filter are available to enhance the precision of the MEMS gyro. The dynamic model of angle movement is studied, the ARMA model describing random drift is established based on time series analysis method, and a modified self-adapted Kalman filter is designed for the signal processing. Finally, the random drift is distinguished and analyzed clearly by Allan variance. It is included that the above method can effectively eliminate the random drift and improve the precision of MEMS gyro.

Citation Download Citation

Xiao-yu Jiang, Yan-tao Zong, Xi Wang, Zhuo Chen, and Zhong-xuan Liu "Precision improving solutions based on ARMA model and modified self-adapted Kalman filter for MEMS gyro", Proc. SPIE 7853, Advanced Sensor Systems and Applications IV, 78533W (9 November 2010); https://doi.org/10.1117/12.871790

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