Bias Prediction for MEMS Gyroscopes

被引:55
作者
Kirkko-Jaakkola, Martti [1 ]
Collin, Jussi [1 ]
Takala, Jarmo [1 ]
机构
[1] Tampere Univ Technol, Dept Comp Syst, FIN-33101 Tampere, Finland
关键词
1/f noise; calibration; gyroscopes; microelectromechanical systems; navigation; stochastic processes;
D O I
10.1109/JSEN.2012.2185692
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
MEMS gyroscopes are gaining popularity because of their low manufacturing costs in large quantities. For navigation system engineering, this presents a challenge because of strong nonstationary noise processes, such as 1/f noise, in the output of MEMS gyros. In practice, on-the-fly calibration is often required before the gyroscope data are useful and comparable to more expensive optical gyroscopes. In this paper, we focus on an important part of MEMS gyro processing, i.e., predicting the future bias given calibration data with known (usually zero) input. We derive prediction algorithms based on Kalman filtering and the computation of moving averages, and compare their performance against simple averaging of the calibration data based on both simulations and real measured data. The results show that it is necessary to model fractional noise in order to consistently predict the bias of a modern MEMS gyro, but the complexity of the Kalman filter approach makes other methods, such as the moving averages, appealing.
引用
收藏
页码:2157 / 2163
页数:7
相关论文
共 34 条
[1]  
Abeywardena D. M. W., 2010, Proceedings of the 2010 5th International Conference on Information and Automation for Sustainability (ICIAfS), P424, DOI 10.1109/ICIAFS.2010.5715699
[2]   STATISTICS OF ATOMIC FREQUENCY STANDARDS [J].
ALLAN, DW .
PROCEEDINGS OF THE INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS, 1966, 54 (02) :221-&
[3]  
Analog Devices Inc, 2004, ADXRS150 DAT SHEET
[4]  
[Anonymous], 1996, 6471995 IEEE
[5]  
[Anonymous], THESIS
[6]  
[Anonymous], 2001, 5282001 IEEE
[7]   Inertial Sensor Technology Trends [J].
Barbour, Neil ;
Schmidt, George .
IEEE SENSORS JOURNAL, 2001, 1 (04) :332-339
[8]  
Beran J., 1994, Statistics for Long-Memory Processes
[9]   The aiding of a low-cost strapdown inertial measurement unit using vehicle model constraints for land vehicle applications [J].
Dissanayake, G ;
Sukkarieh, S ;
Nebot, E ;
Durrant-Whyte, H .
IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION, 2001, 17 (05) :731-747
[10]   Temperature variation effects on stochastic characteristics for low-cost MEMS-based inertial sensor error [J].
El-Diasty, M. ;
El-Rabbany, A. ;
Pagiatakis, S. .
MEASUREMENT SCIENCE AND TECHNOLOGY, 2007, 18 (11) :3321-3328