Next Article in Journal
Sparse Auto-Calibration for Radar Coincidence Imaging with Gain-Phase Errors
Previous Article in Journal
Calibration of Kinect for Xbox One and Comparison between the Two Generations of Microsoft Sensors
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(11), 27590-27610; doi:10.3390/s151127590

Dynamic Performance Comparison of Two Kalman Filters for Rate Signal Direct Modeling and Differencing Modeling for Combining a MEMS Gyroscope Array to Improve Accuracy

1
Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace, Northwestern Polytechnical University, No. 127 Youyi West Road, Xi’an 710072, China
2
Xi’an Research Institute of High Technology, Hongqing Town, Xi’an 710025, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 20 August 2015 / Revised: 21 October 2015 / Accepted: 23 October 2015 / Published: 30 October 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [1294 KB, uploaded 30 October 2015]   |  

Abstract

In this paper, the performance of two Kalman filter (KF) schemes based on the direct estimated model and differencing estimated model for input rate signal was thoroughly analyzed and compared for combining measurements of a sensor array to improve the accuracy of microelectromechanical system (MEMS) gyroscopes. The principles for noise reduction were presented and KF algorithms were designed to obtain the optimal rate signal estimates. The input rate signal in the direct estimated KF model was modeled with a random walk process and treated as the estimated system state. In the differencing estimated KF model, a differencing operation was established between outputs of the gyroscope array, and then the optimal estimation of input rate signal was achieved by compensating for the estimations of bias drifts for the component gyroscopes. Finally, dynamic simulations and experiments with a six-gyroscope array were implemented to compare the dynamic performance of the two KF models. The 1σ error of the gyroscopes was reduced from 1.4558°/s to 0.1203°/s by the direct estimated KF model in a constant rate test and to 0.5974°/s by the differencing estimated KF model. The estimated rate signal filtered by both models could reflect the amplitude variation of the input signal in the swing rate test and displayed a reduction factor of about three for the 1σ noise. Results illustrate that the performance of the direct estimated KF model is much higher than that of the differencing estimated KF model, with a constant input signal or lower dynamic variation. A similarity in the two KFs’ performance is observed if the input signal has a high dynamic variation. View Full-Text
Keywords: MEMS gyroscope; Kalman filtering; sensor array; direct model; differencing model; performance comparison; optimal estimation MEMS gyroscope; Kalman filtering; sensor array; direct model; differencing model; performance comparison; optimal estimation
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 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

Yuan, G.; Yuan, W.; Xue, L.; Xie, J.; Chang, H. Dynamic Performance Comparison of Two Kalman Filters for Rate Signal Direct Modeling and Differencing Modeling for Combining a MEMS Gyroscope Array to Improve Accuracy. Sensors 2015, 15, 27590-27610.

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