Combining numerous uncorrelated MEMS gyroscopes for accuracy improvement based on an optimal kalman filter

Honglong Chang, Liang Xue, Chengyu Jiang, Michael Kraft, Weizheng Yuan

Research output: Contribution to journalArticlepeer-review

69 Scopus citations

Abstract

In this paper, an approach to improve the accuracy of microelectromechanical systems (MEMS) gyroscopes by combining numerous uncorrelated gyroscopes is presented. A Kalman filter (KF) is used to fuse the output signals of several uncorrelated sensors. The relationship between the KF bandwidth and the angular rate input is quantitatively analyzed. A linear model is developed to choose suitable system parameters for a dynamic application of the concept. Simulation and experimental tests of a six-gyroscope array proved that the presented approach was effective to improve the MEMS gyroscope accuracy. The experimental results indicate that six identical gyroscopes with a noise density of 0.11°/h and a bias instability of 62°/s can be combined to form a virtual gyroscope with a noise density of 0.03°/h and a bias instability of 16.8°/s. The accuracy improvement is better than that of a simple averaging process of the individual sensors.

Original languageEnglish
Article number6214600
Pages (from-to)3084-3093
Number of pages10
JournalIEEE Transactions on Instrumentation and Measurement
Volume61
Issue number11
DOIs
StatePublished - 2012

Keywords

  • Array signal processing
  • filtering
  • gyroscope
  • microelectromechanical devices
  • random noise

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