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DALIAN UNIVERSITY OF TECHNOLOGY Login 中文
Wang Zhelong

Professor
Supervisor of Doctorate Candidates
Supervisor of Master's Candidates


Main positions:Professor, Head of Lab of Intelligent System
Other Post:自动化技术研究所所长
Gender:Male
Alma Mater:University of Durham
Degree:Doctoral Degree
School/Department:School of Control Science and Engineering
Discipline:Control Theory and Control Engineering. Pattern Recognition and Intelligence System. Detection Technology and Automation Device
Business Address:Lab of Intelligent System
http://lis.dlut.edu.cn/

Contact Information:0411-84709010 wangzl@dlut.edu.cn
E-Mail:wangzl@dlut.edu.cn
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A time-controllable Allan variance method for MEMS IMU

Hits : Praise

Indexed by:期刊论文

Date of Publication:2013-01-01

Journal:INDUSTRIAL ROBOT-AN INTERNATIONAL JOURNAL

Included Journals:SCIE、EI、Scopus

Volume:40

Issue:2

Page Number:111-120

ISSN No.:0143-991X

Key Words:Stability (control theory); Noise; Allan variance; Micro-electro-mechanical systems; Inertial measurement unit; MEMS IMU; Noise coefficient; Gyroscope

Abstract:Purpose - The purpose of this paper is to reduce the calculation burden and speed up the estimation process of Allan variance method while ensuring the exactness of the analysis results.
   Design/methodology/approach - A series of six-hour static tests have been implemented at room temperature, and the static measurements have been collected from MEMS IMU. In order to characterize the various types of random noise terms for the IMU, the basic definition and main procedure of the Allan variance method are investigated. Unlike the normal Allan variance method, which has the shortcomings of processing large data sets and requiring long computation time, a modified Allan variance method is proposed based on the features of data distribution in the log-log plot of the Allan standard deviation versus the averaging time.
   Findings - Experiment results demonstrate that the modified Allan variance method can effectively estimate the noise coefficients for MEMS IMU, with controllable computation time and acceptable estimation accuracy.
   Originality/value - This paper proposes a time-controllable Allan variance method which can quickly and accurately identify different noise terms imposed by the stochastic fluctuations.