MEMS gyro random drift model parameter identification based on two-stage recursive least squares method

Zhaohua Liu; Jiabin Chen; Yuliang Mao; Chunlei Song

doi:10.4028/www.scientific.net/AMM.220-223.1044

MEMS gyro random drift model parameter identification based on two-stage recursive least squares method

Zhaohua Liu^*, Jiabin Chen, Yuliang Mao, Chunlei Song

^*Corresponding author for this work

School of Automation

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

Autoregressive moving average model (ARMA) was usually used for gyro random drift modeling. Because gyro random drift was a non-stationary, weak non-linear and time-variant random signal, model parameters were random and time-variant, too. For improving precision of gyro and reducing effects of random drift, this paper adopted two-stage recursive least squares method for ARMA parameter estimation. This method overcame the shortcomings of the conventional recursive extended least squares (RELS) algorithm. At the same time, the forgetting factor was introduced to adapt the model parameters change. The simulation experimental results showed that this method is effective.

Original language	English
Title of host publication	Advances in Manufacturing Technology
Pages	1044-1047
Number of pages	4
DOIs	https://doi.org/10.4028/www.scientific.net/AMM.220-223.1044
Publication status	Published - 2012
Event	2nd International Conference on Advanced Design and Manufacturing Engineering, ADME 2012 - Taiyuan, China Duration: 16 Aug 2012 → 18 Aug 2012

Publication series

Name	Applied Mechanics and Materials
Volume	220-223
ISSN (Print)	1660-9336
ISSN (Electronic)	1662-7482

Conference

Conference	2nd International Conference on Advanced Design and Manufacturing Engineering, ADME 2012
Country/Territory	China
City	Taiyuan
Period	16/08/12 → 18/08/12

Keywords

ARMA
Gyro
Random drift
Two-stage recursive least squares

Access to Document

10.4028/www.scientific.net/AMM.220-223.1044

Cite this

@inproceedings{d3a1fd9bf3494a19ba4ab2a5c84f9708,

title = "MEMS gyro random drift model parameter identification based on two-stage recursive least squares method",

abstract = "Autoregressive moving average model (ARMA) was usually used for gyro random drift modeling. Because gyro random drift was a non-stationary, weak non-linear and time-variant random signal, model parameters were random and time-variant, too. For improving precision of gyro and reducing effects of random drift, this paper adopted two-stage recursive least squares method for ARMA parameter estimation. This method overcame the shortcomings of the conventional recursive extended least squares (RELS) algorithm. At the same time, the forgetting factor was introduced to adapt the model parameters change. The simulation experimental results showed that this method is effective.",

keywords = "ARMA, Gyro, Random drift, Two-stage recursive least squares",

author = "Zhaohua Liu and Jiabin Chen and Yuliang Mao and Chunlei Song",

year = "2012",

doi = "10.4028/www.scientific.net/AMM.220-223.1044",

language = "English",

isbn = "9783037855034",

series = "Applied Mechanics and Materials",

pages = "1044--1047",

booktitle = "Advances in Manufacturing Technology",

note = "2nd International Conference on Advanced Design and Manufacturing Engineering, ADME 2012 ; Conference date: 16-08-2012 Through 18-08-2012",

}

Liu, Z, Chen, J, Mao, Y & Song, C 2012, MEMS gyro random drift model parameter identification based on two-stage recursive least squares method. in Advances in Manufacturing Technology. Applied Mechanics and Materials, vol. 220-223, pp. 1044-1047, 2nd International Conference on Advanced Design and Manufacturing Engineering, ADME 2012, Taiyuan, China, 16/08/12. https://doi.org/10.4028/www.scientific.net/AMM.220-223.1044

TY - GEN

T1 - MEMS gyro random drift model parameter identification based on two-stage recursive least squares method

AU - Liu, Zhaohua

AU - Chen, Jiabin

AU - Mao, Yuliang

AU - Song, Chunlei

PY - 2012

Y1 - 2012

N2 - Autoregressive moving average model (ARMA) was usually used for gyro random drift modeling. Because gyro random drift was a non-stationary, weak non-linear and time-variant random signal, model parameters were random and time-variant, too. For improving precision of gyro and reducing effects of random drift, this paper adopted two-stage recursive least squares method for ARMA parameter estimation. This method overcame the shortcomings of the conventional recursive extended least squares (RELS) algorithm. At the same time, the forgetting factor was introduced to adapt the model parameters change. The simulation experimental results showed that this method is effective.

AB - Autoregressive moving average model (ARMA) was usually used for gyro random drift modeling. Because gyro random drift was a non-stationary, weak non-linear and time-variant random signal, model parameters were random and time-variant, too. For improving precision of gyro and reducing effects of random drift, this paper adopted two-stage recursive least squares method for ARMA parameter estimation. This method overcame the shortcomings of the conventional recursive extended least squares (RELS) algorithm. At the same time, the forgetting factor was introduced to adapt the model parameters change. The simulation experimental results showed that this method is effective.

KW - ARMA

KW - Gyro

KW - Random drift

KW - Two-stage recursive least squares

UR - http://www.scopus.com/inward/record.url?scp=84870576501&partnerID=8YFLogxK

U2 - 10.4028/www.scientific.net/AMM.220-223.1044

DO - 10.4028/www.scientific.net/AMM.220-223.1044

M3 - Conference contribution

AN - SCOPUS:84870576501

SN - 9783037855034

T3 - Applied Mechanics and Materials

SP - 1044

EP - 1047

BT - Advances in Manufacturing Technology

T2 - 2nd International Conference on Advanced Design and Manufacturing Engineering, ADME 2012

Y2 - 16 August 2012 through 18 August 2012

ER -

MEMS gyro random drift model parameter identification based on two-stage recursive least squares method

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this