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A Standard Testing and Calibration Procedure for Low Cost MEMS Inertial Sensors and Units

Published online by Cambridge University Press:  25 March 2008

P. Aggarwal ,
Z. Syed ,
X. Niu  and
N. El-Sheimy
P. Aggarwal*
Affiliation:
(University of Calgary, Canada)
Z. Syed
Affiliation:
(University of Calgary, Canada)
X. Niu
Affiliation:
(University of Calgary, Canada)
N. El-Sheimy
Affiliation:
(University of Calgary, Canada)
*
(Email: [email protected])
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Abstract

Navigation involves the integration of methodologies and systems for estimating the time varying position and attitude of moving objects. Inertial Navigation Systems (INS) and the Global Positioning System (GPS) are among the most widely used navigation systems. The use of cost effective MEMS based inertial sensors has made GPS/INS integrated navigation systems more affordable. However MEMS sensors suffer from various errors that have to be calibrated and compensated to get acceptable navigation results. Moreover the performance characteristics of these sensors are highly dependent on the environmental conditions such as temperature variations. Hence there is a need for the development of accurate, reliable and efficient thermal models to reduce the effect of these errors that can potentially degrade the system performance. In this paper, the Allan variance method is used to characterize the noise in the MEMS sensors. A six-position calibration method is applied to estimate the deterministic sensor errors such as bias, scale factor, and non-orthogonality. An efficient thermal variation model is proposed and the effectiveness of the proposed calibration methods is investigated through a kinematic van test using integrated GPS and MEMS-based inertial measurement unit (IMU).

Keywords

Thermal modelGPSINSMEMS
Type
Research Article
Information
The Journal of Navigation , Volume 61 , Issue 2 , April 2008 , pp. 323 - 336
Copyright
Copyright © The Royal Institute of Navigation 2008

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References

REFERENCES

Aggarwal, P., Syed, Z., Niu, X. and El-Sheimy, N. (2007): Thermal Calibration of Low Cost MEMS Sensors for Integrated Positioning, Navigation Systems, The Institute of Navigation National Technical Meeting, San Diego, California, USA, January 22–24.Google Scholar
Aggarwal, P., Syed, Z., Niu, X. and El-Sheimy, N. (2006): Cost-effective Testing and Calibration of Low Cost MEMS Sensors for Integrated Positioning, Navigation and Mapping Systems. XXIII International FIG Congress, Munich, Germany, October 8–13.Google Scholar
El-Sheimy, N. (2003). Inertial techniques and INS/DGPS integration: Lecture Notes ENGO 623. Dept. of Geomatics Eng., University of Calgary, Calgary, Canada.Google Scholar
El-Diasty, M., El-Rabbany, A. and Pagiatakis, S. (2006), Stochastic Characteristics of Temperature-Dependent MEMS-Based Inertial Sensor Error. The Institute of Navigation National Technical Meeting, Monterey, California, USA, January 18–20.Google Scholar
Godha, S. (2006), Performance Evaluation of Low Cost MEMS-Based IMU Integrated with GPS for Land Vehicle Navigation Application, M.Sc. Thesis, Department of Geomatics Engineering, University of Calgary, UCGE Report 20239.Google Scholar
Hou, H. (2004), Modeling Inertial Sensors Errors Using Allan Variance, M.Sc. thesis, Department of Geomatics Engineering, University of Calgary, Canada, UCGE Report 20201.Google Scholar
IEEE Std 952-1997 IEEE Standard Specification Format Guide and Test Procedure for Single–Axis Interferometric Fiber Optic Gyros.Google Scholar
Nassar, S., Niu, X., Aggarwal, P. and El-Sheimy, N. (2006): INS/GPS Sensitivity Analysis Using Different Kalman Filter Approaches. The Institute of Navigation National Technical Meeting, Monterey, California, USA, January 18–20.Google Scholar
Nassar, S. (2003). Improving the Inertial Navigation System (INS) Error Model for INS and INS/DGPS Applications, PhD Thesis, Department of Geomatics Engineering, University of Calgary, Canada, UCGE Report No. 20183Google Scholar
Niu, X., and El-Sheimy, N. (2005). The Development of a Low-cost MEMS IMU/GPS Navigation System for Land Vehicles Using Auxiliary Velocity Updates in the Body Frame. Proceedings of ION GNSS. September 13–16, Long Beach, CA, USA.Google Scholar
Park, M. (2004), Error Analysis and Stochastic Modeling of MEMS based Inertial Sensors for Land Vehicle Navigation Applications, M.Sc. thesis, Department of Geomatics Engineering, University of Calgary, Canada, UCGE Report 20194.Google Scholar
Shcheglov, K., Evans, C., Gutierrez, R. and Tang, T. K. (2000), Temperature dependent characteristics of the JPL silicon MEMS gyroscope. IEEE Aerospace Conference Proceedings, Vol 1, MT, Mar 18–25.Google Scholar
Shin, E. H. and El-Sheimy, N. (2005): In-Motion Alignment of Low-Cost IMUs. European Journal of Navigation, V.3, N.1, pp: 40–50, February.Google Scholar
Titterton, D. H. and Weston, J. L. (1997). Strapdown Inertial Navigation Technology. Peter Peregrinus Ltd, UK.Google Scholar
Walid, A. H. (2005), Accuracy Enhancement of Integrated MEMS-IMU/GPS Systems for Land Vehicular Navigation Applications, PhD Thesis, Department of Geomatics Engineering, University of Calgary, UCGE Report 20207.Google Scholar