Next Article in Journal
NDIR Gas Sensor for Spatial Monitoring of Carbon Dioxide Concentrations in Naturally Ventilated Livestock Buildings
Next Article in Special Issue
Development of a Portable Non-Invasive Swallowing and Respiration Assessment Device
Previous Article in Journal
Afocal Optical Flow Sensor for Reducing Vertical Height Sensitivity in Indoor Robot Localization and Navigation
Previous Article in Special Issue
A PDMS-Based 2-Axis Waterproof Scanner for Photoacoustic Microscopy
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(5), 11222-11238; doi:10.3390/s150511222

An Adaptive Compensation Algorithm for Temperature Drift of Micro-Electro-Mechanical Systems Gyroscopes Using a Strong Tracking Kalman Filter

School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Academic Editor: Stefano Mariani
Received: 7 February 2015 / Revised: 13 April 2015 / Accepted: 29 April 2015 / Published: 13 May 2015
(This article belongs to the Special Issue Modeling, Testing and Reliability Issues in MEMS Engineering)
View Full-Text   |   Download PDF [1818 KB, uploaded 13 May 2015]   |  

Abstract

We present an adaptive algorithm for a system integrated with micro-electro-mechanical systems (MEMS) gyroscopes and a compass to eliminate the influence from the environment, compensate the temperature drift precisely, and improve the accuracy of the MEMS gyroscope. We use a simplified drift model and changing but appropriate model parameters to implement this algorithm. The model of MEMS gyroscope temperature drift is constructed mostly on the basis of the temperature sensitivity of the gyroscope. As the state variables of a strong tracking Kalman filter (STKF), the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass. These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to −2° in the dynamic outdoor experiment. This demonstrates that the proposed algorithm exhibits strong adaptability to a changing temperature, and performs significantly better than KF and MLR to compensate the temperature drift of a gyroscope and eliminate the influence of temperature variation. View Full-Text
Keywords: strong tracking Kalman filter; bias; compass; MEMS gyroscope strong tracking Kalman filter; bias; compass; MEMS gyroscope
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Feng, Y.; Li, X.; Zhang, X. An Adaptive Compensation Algorithm for Temperature Drift of Micro-Electro-Mechanical Systems Gyroscopes Using a Strong Tracking Kalman Filter. Sensors 2015, 15, 11222-11238.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top