Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-10T17:39:22.110Z Has data issue: false hasContentIssue false
  • English
  • Français

A Universal Approach for Processing any MEMS Inertial Sensor Configuration for Land-Vehicle Navigation

Published online by Cambridge University Press:  20 April 2007

Xiaoji Niu ,
Sameh Nasser ,
Chris Goodall  and
Naser El-Sheimy
Xiaoji Niu
Affiliation:
(The University of Calgary, Canada) (Email: xniu@geomatics.ucalgary.ca)
Sameh Nasser
Affiliation:
(The University of Calgary, Canada) (Email: xniu@geomatics.ucalgary.ca)
Chris Goodall
Affiliation:
(The University of Calgary, Canada) (Email: xniu@geomatics.ucalgary.ca)
Naser El-Sheimy
Affiliation:
(The University of Calgary, Canada) (Email: xniu@geomatics.ucalgary.ca)
Get access Rights & Permissions [Opens in a new window]

Abstract

Recent navigation systems integrating GPS with Micro-Electro-Mechanical Systems (MEMS) Inertial Measuring Units (IMUs) have shown promising results for several applications based on low-cost devices such as vehicular and personal navigation. However, as a trend in the navigation market, some applications require further reductions in size and cost. To meet such requirements, a MEMS full IMU configuration (three gyros and three accelerometers) may be simplified. In this context, different partial IMU configurations such as one gyro plus three accelerometers or one gyro plus two accelerometers could be investigated. The main challenge in this case is to develop a specific navigation algorithm for each configuration since this is a time-consuming and costly task. In this paper, a universal approach for processing any MEMS sensor configuration for land vehicular navigation is introduced. The proposed method is based on the assumption that the omitted sensors provide relatively less navigation information and hence, their output can be replaced by pseudo constant signals plus noise. Using standard IMU/GPS navigation algorithms, signals from existing sensors and pseudo signals for the omitted sensors are processed as a full IMU. The proposed approach is tested using land-vehicle MEMS/GPS data and implemented with different sensor configurations. Compared to the full IMU case, the results indicate the differences are within the expected levels and that the accuracy obtained meets the requirements of several land-vehicle applications.

Keywords

GPS/INSMEMSVehicle NavigationPartial IMU Configurations
Type
Research Article
Information
The Journal of Navigation , Volume 60 , Issue 2 , May 2007 , pp. 233 - 245
Copyright
Copyright © The Royal Institute of Navigation 2007

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Brandt, A. and Gardner, J. F. (1998). Constrained Navigation Algorithms for Strapdown Inertial Navigation Systems with Reduced Set of Sensors. Proceedings of the American Control Conference, Philadelphia, Pennsylvania, USA, June 24–26, V.3, pp. 1848–1852.CrossRefGoogle Scholar
El-Sheimy, N. and Niu, X. (2004). The Development of Low-Cost MEMS-Based IMU for Land Vehicle Navigation Applications. The GEOIDE 6th Annual Meeting, Gatineau, Québec, Canada, May 30-June 1. (CD Proceedings)Google Scholar
Mayhew, D. M. (1999). Multi-rate Sensor Fusion for GPS Navigation Using Kalman Filtering. MSc Thesis, Department of Electrical and Computer Engineering, Faculty of the Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA.Google Scholar
Nassar, S., Niu, X., Aggarwal, P. and El-Sheimy, N. (2006). INS/GPS Sensitivity Analysis Using Different Kalman Filter Approaches. Proceedings of the Institute of Navigation National Technical Meeting (ION NTM 2006), Monterey, California, USA, January 18–20. pp. 993–1001.Google Scholar
Niu, X. and El-Sheimy, N. (2005). Development of a Low-cost MEMS IMU/GPS Navigation System for Land Vehicles Using Auxiliary Velocity Updates in the Body Frame. Proceedings of the Institute of Navigation Satellite Division Technical Meeting (ION GPS 2005), Long Beach, California, USA, September 13–16. pp. 2003–2012.Google Scholar
Phuyal, B. (2004). An Experiment for a 2-D and 3-D GPS/INS Configuration for Land Vehicle Applications. Proceedings of the IEEE Position Location and Navigation Symposium (PLANS 2004), Monterey, California, USA, April 26–29. pp. 148–152.CrossRefGoogle Scholar
Shin, E. H. and El-Sheimy, N. (2004). Aided Inertial Navigation System (AINS™) Toolbox for MatLab™ Software, INS/GPS Integration Software, Mobile Multi-Sensor Systems (MMSS) Research Group, the University of Calgary, Alberta, Canada. [http://mms.geomatics.ucalgary.ca/Research/research.htm]Google Scholar