MEMS Gyro Signal De-noising Method Based on Extended Recursive Least Square

被引：0

作者：

He Xiaofeng ^{[1
]}

Hu Xiaoping ^{[1
]}

Wu Meiping ^{[1
]}

Yu Huiying ^{[1
]}

Qu Haili ^{[1
]}

机构：

[1] Natl Univ Def Technol, Coll Mech Engn & Automat, Changsha 410073, Hunan, Peoples R China

来源：

PROCEEDINGS OF THE 27TH CHINESE CONTROL CONFERENCE, VOL 3 | 2008年

关键词：

Recursive least square; MEMS gyro; De-noising; ARMA;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper discusses the design of extended recursive least square method based on time series analysis in order to overcome large noise and low precision of MEMS gyro. The method adopts the forgetting factor-based recursive least square which can work well even with uncertain noises. Firstly, ARMA models are used to model gyro random drifts. Secondly, variable forgetting factors enhance the robustness of extended recursive least square approach. Some experiments are carried out and the results show that the proposed method advances the performance of MEMS gyro signal de-noising. It gains better accuracy and better robustness than traditional Kalman filter.

引用

页码：403 / 406

页数：4