Next Article in Journal
Extrusion-Based 3D Printing of Microfluidic Devices for Chemical and Biomedical Applications: A Topical Review
Next Article in Special Issue
Development of a Thermoelectric and Electromagnetic Hybrid Energy Harvester from Water Flow in an Irrigation System
Previous Article in Journal
A High-Dynamic-Range Switched-Capacitor Sigma-Delta ADC for Digital Micromechanical Vibration Gyroscopes
Previous Article in Special Issue
A Study on Microturning with Electrochemical Assistance of the Cutting Process
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle

Random Error Analysis of MEMS Gyroscope Based on an Improved DAVAR Algorithm

Department of Vehicle and Electrical Engineering, Army Engineering University, Shijiazhuang 050003, China
*
Author to whom correspondence should be addressed.
Micromachines 2018, 9(8), 373; https://doi.org/10.3390/mi9080373
Received: 14 June 2018 / Revised: 17 July 2018 / Accepted: 20 July 2018 / Published: 27 July 2018
(This article belongs to the Special Issue Advanced MEMS/NEMS Technology)

Abstract

In view that traditional dynamic Allan variance (DAVAR) method is difficult to make a good balance between dynamic tracking capabilities and the confidence of the estimation. And the reason is the use of a rectangular window with the fixed window length to intercept the original signal. So an improved dynamic Allan variance method was proposed. Compared with the traditional Allan variance method, this method can adjust the window length of the rectangular window adaptively. The data in the beginning and terminal interval was extended with the inverted mirror extension method to improve the utilization rate of the interval data. And the sliding kurtosis contribution coefficient and kurtosis were introduced to adjust the length of the rectangular window by sensing the content of shock signal in terminal interval. The method analyzed the window length change factor in different stable conditions and adjusted the rectangular window’s window length according to the kurtosis, sliding kurtosis contribution coefficient. The test results show that the more the kurtosis stability threshold was close to 3, the stronger the dynamic tracking ability of DAVAR would be. But the kurtosis stability threshold was too close to 3, there was a misjudgement in kurtosis analysis of signal stability, resulting in distortion of DAVAR analysis results. When using the improved DAVAR method, the kurtosis stability threshold can be close to 3 to improve the tracking ability and the estimation confidence in stable interval. Therefore, it solved the problem that the dynamic Allan variance tracking ability and confidence level were difficult to take into account, and also solved the problem of misjudgement in the stability analysis of kurtosis. View Full-Text
Keywords: dynamic Allan variance; kurtosis; sliding kurtosis contribution coefficient; MEMS dynamic Allan variance; kurtosis; sliding kurtosis contribution coefficient; MEMS
Figures

Figure 1

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

Share & Cite This Article

MDPI and ACS Style

Song, J.; Shi, Z.; Wang, L.; Wang, H. Random Error Analysis of MEMS Gyroscope Based on an Improved DAVAR Algorithm. Micromachines 2018, 9, 373.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Micromachines EISSN 2072-666X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top