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Study of the characteristics of random errors in measurements by MEMS inertial sensors

Study of the characteristics of random errors in measurements by MEMS inertial sensors This paper considers a stochastic analysis of measurement errors in MotionNode inertial measurement units (IMUs). The sensors of this device are based on the microelectromechanical system (MEMS) technology. The use of the Allan variance algorithm for identifying the error’s nature and the stochastic modeling of this error make it possible to substantially reduce the measurement errors by sensors. Based on the results of this study, we elaborated a number of recommendations for increasing the efficiency of the use of MEMS inertial sensors for problems of the navigation of moving objects. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Automatic Control and Computer Sciences Springer Journals

Study of the characteristics of random errors in measurements by MEMS inertial sensors

Bistrov, V.
Automatic Control and Computer Sciences , Volume 45 (5) – Nov 9, 2011
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Publisher
Springer Journals
Copyright
Copyright © 2011 by Allerton Press, Inc.
Subject
Computer Science; Control Structures and Microprogramming
ISSN
0146-4116
eISSN
1558-108X
DOI
10.3103/S0146411611050051
Publisher site
See Article on Publisher Site

Abstract

This paper considers a stochastic analysis of measurement errors in MotionNode inertial measurement units (IMUs). The sensors of this device are based on the microelectromechanical system (MEMS) technology. The use of the Allan variance algorithm for identifying the error’s nature and the stochastic modeling of this error make it possible to substantially reduce the measurement errors by sensors. Based on the results of this study, we elaborated a number of recommendations for increasing the efficiency of the use of MEMS inertial sensors for problems of the navigation of moving objects.

Journal

Automatic Control and Computer Sciences – Springer Journals

Published: Nov 9, 2011

References

  • MEMS-Based Integrated Navigation
    Aggarwal, P.; Syed, Z.; Noureldin, A.; El-Sheimy, N.
  • Online Estimation of State Space Error Model for MEMS IMU
    Saini
  • INS/GPS Integration for Geomatics Department of Geomatics Engineering
    Schwarz, K.P.; Wei, M.
  • Introduction to Random Signals and Applied Kalman Filtering with Matlab Exercises and Solutions
    Brown, R.G.; Hwang, a. P. Y.C.
  • An Integrated MEMS Gyroscope Array with Higher Accuracy Output
    Chang, H.
  • Groves Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems
    Paul, D.
  • Stochastic Modeling of MEMS Inertial Sensors
    Petkov, P.; Slavov, T.