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

被引：3

作者：

Jiang Xiao-yu ^{[1
]}

Zong Yan-tao ^{[1
]}

Wang Xi ^{[1
]}

Chen Zhuo ^{[1
]}

Liu Zhong-xuan ^{[1
]}

机构：

[1] Acad Armored Force Engn, Dept Control Engn, Beijing, Peoples R China

来源：

ADVANCED SENSOR SYSTEMS AND APPLICATIONS IV | 2010年 / 7853卷

关键词：

MEMS gyro; random drift; Kalman filter; Allan variance analysis;

D O I：

10.1117/12.871790

中图分类号：

TP7 [遥感技术];

学科分类号：

081102 ; 0816 ; 081602 ; 083002 ; 1404 ;

摘要：

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.

引用

页数：6