Share Email Print
cover

Proceedings Paper

Precision improving solutions based on ARMA model and modified self-adapted Kalman filter for MEMS gyro
Author(s): Xiao-yu Jiang; Yan-tao Zong; Xi Wang; Zhuo Chen; Zhong-xuan Liu
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper 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.

Paper Details

Date Published: 9 November 2010
PDF: 6 pages
Proc. SPIE 7853, Advanced Sensor Systems and Applications IV, 78533W (9 November 2010); doi: 10.1117/12.871790
Show Author Affiliations
Xiao-yu Jiang, Academy of Armored Force Engineering (China)
Yan-tao Zong, Academy of Armored Force Engineering (China)
Xi Wang, Academy of Armored Force Engineering (China)
Zhuo Chen, Academy of Armored Force Engineering (China)
Zhong-xuan Liu, Academy of Armored Force Engineering (China)


Published in SPIE Proceedings Vol. 7853:
Advanced Sensor Systems and Applications IV
Brian Culshaw; Yanbiao Liao; Anbo Wang; Xiaoyi Bao; Xudong Fan; Lin Zhang, Editor(s)

© SPIE. Terms of Use
Back to Top