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Optimal Wiener Filter for a Body Mounted Inertial Attitude Sensor

Published online by Cambridge University Press:  26 June 2008

Peter Rizun
Peter Rizun*
Affiliation:
(University of Calgary)
*
(Email: prizun@ucalgary.ca)
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Abstract

An optimal attitude estimator is presented for a human body-mounted inertial measurement unit employing orthogonal triads of gyroscopes, accelerometers and magnetometers. The estimator continuously fuses gyroscope and accelerometer measurements together in a manner that minimizes the mean square error in the estimate of the gravity vector, based on known spectral characteristics for the gyroscope noise and the linear acceleration of points on the human body. The gyroscope noise is modelled as a white noise process of power spectral density δn2/2 while the linear acceleration is modelled as the derivative of a band-limited white noise process of power spectral density δv2/2. The estimator is robust to centripetal acceleration and guaranteed to have zero mean error regardless of the motion of the sensor. The mean square angular error in attitude is shown to be independent of the module's angular velocity and equal to 21/2g−1/2δn3/2δv1/2.

Keywords

Attitude estimationInertial/magnetic motion captureMultisensory fusionWiener filtering
Type
Research Article
Information
The Journal of Navigation , Volume 61 , Issue 3 , July 2008 , pp. 455 - 472
Copyright
Copyright © The Royal Institute of Navigation 2008

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References

REFERENCES

Analytic Sciences Corporation, Technical Staff (1974). Applied Optimal Estimation. Gelb, Arthur (Ed.). MIT Press.Google Scholar
Bachmann, E. (2000). Inertial and magnetic angle tracking of limb segments for inserting humans into synthetic environments. PhD thesis. Naval Postgraduate School. Monterey, California, USA.Google Scholar
Bachmann, E., Yun, X., McKinney, D., McGhee, R. and Zyda, M. (2003). Design and Implementation of MARG Sensors for 3-DOF Orientation Measurement of Rigid Bodies. Proceedings of the 2003 IEEE International Conference On Robotics & Automation. Taipei, Taiwan, September 14–19.Google Scholar
Bernmark, E., and Wiktorin, C. (2002). A triaxial accelerometer for measuring arm movements. Appl. Ergon., 33, 541–547.CrossRefGoogle ScholarPubMed
Biosyn Systems Inc. (2007). http://www.biosyn.ca.Google Scholar
Burdea, G. (1996). Force and Touch Feedback for Virtual Reality. Wiley: New York.Google Scholar
Butterworth, S. (1930). On the theory of filter amplifiers. Wireless Engineer, 7, 536–541.Google Scholar
Foxlin, E. (1993). Inertial Head-Tracking. M.Sc. Thesis, Department of Electrical Engineering and Computer Science, MIT, MA, USA.Google Scholar
Foxlin, E. (1996). Inertial head-tracker sensor fusion by a complementary separate-bias Kalman filter. Proceedings of VRAIS.Google Scholar
Godha, S., Lachapelle, G. and Cannon, M. E. (2006). Integrated GPS/INS system for pedestrian navigation in a signal degraded environment. Proceedings of ION GNSS, Fort Worth, Texas.Google Scholar
Goldstein, H. (1980). Classical Mechanics (second edition). Addison-Wesley.Google Scholar
Luinge, H. J., Veltink, P. H. (2005). Measuring orientation of human segments using miniature gyroscopes and accelerometers. Med. Biol. Eng. Comput., 43, 273–282.CrossRefGoogle ScholarPubMed
Luinge, H. J., and Veltink, P. H. (2004). Inclination measurement of human movement using a 3D accelerometer with autocalibration. IEEE Trans. Neural Syst. Rehabil. Eng., 12, 112–121.Google Scholar
Luinge, H. (2002). Inertial sensing of human motion. PhD thesis. University of Twente, Netherlands.Google Scholar
Mayagoitia, R. E., Nene, A. V., Veltink, P. H. (2002). Accelerometer and rate gyroscope measurement of kinematics: an inexpensive alternative to optical motion analysis systems. Journal of Biomechanics, 35, 537–542.Google Scholar
Roetenberg, D. (2006). Inertial and magnetic sensing of human motion. PhD thesis. University of Twente, Netherlands.Google Scholar
Roetenberg, D., Slycke, P. and Veltink, P. (2007). Ambulatory position and orientation tracking fusing magnetic and inertial sensing. IEEE Transactions on Biomedical Engineering. 54 (5) 883–890.Google Scholar
Setoodeh, P., Khayatian, A., Farjah, E. (2004). Attitude estimation by separate-bias kalman filter-based data fusion. The Journal of Navigation, 57, 261–273.CrossRefGoogle Scholar
Simon, D (2006). Optimal State Estimation: Kalman, H-infinity, and Nonlinear Approaches. John Wiley & Sons.CrossRefGoogle Scholar
Stirling, R., Fyfe, K. and Lachapelle, G. (2005). Evaluation of a new method of heading estimation for pedestrian dead reckoning using shoe mounted sensors. The Journal of Navigation, 58, 31–45.CrossRefGoogle Scholar
Vaganay, J., Aldon, M. J., Fournier, A. (1993). Mobile robot attitude estimation by fusion of inertial data. Proceedings of the International Conference on Robotics and Automation.Google Scholar
Wiener, N. (1949). Extrapolation, interpolation, and smoothing of stationary time series with engineering applications. MIT Press.CrossRefGoogle Scholar
Williamson, R. and Andrews, B. J. (2001). Detecting absolute human knee angle and angular velocity using accelerometers and rate gyroscopes. Med. Biol. Eng. Comput., 39, 294–302.Google Scholar
Yun, X., Lizarraga, M., Bachmann, E. R. and McGhee, R. B. (2003). An improved quaternion-based Kalman filter for real-time tracking of rigid body orientation. Proceedings of the International Conference on Intelligent Robots and Systems, Las Vegas, Nevada.Google Scholar
Yun, X., Aparicio, C., Bachmann, E. R. and McGhee, R. B. (2005). Implementation and experimental results of a quaternion-based Kalman filter for human body motion tracking. Proceedings of the International Conference on Robotics and Automation, Barcelona, Spain.Google Scholar