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An Enhanced 3D Multi-Sensor Integrated Navigation System for Land-Vehicles

Published online by Cambridge University Press:  12 March 2014

Mohamed Maher Atia ,
Tashfeen Karamat  and
Aboelmagd Noureldin
Mohamed Maher Atia*
Affiliation:
(Royal Military College of Canada: Electrical & Computer Engineering Dept. NavINST Research Lab Kingston, ON, Canada)
Tashfeen Karamat
Affiliation:
(Royal Military College of Canada: Electrical & Computer Engineering Dept. NavINST Research Lab Kingston, ON, Canada)
Aboelmagd Noureldin
Affiliation:
(Royal Military College of Canada: Electrical & Computer Engineering Dept. NavINST Research Lab Kingston, ON, Canada)
*
(E-mail: mohamed.maher.atia@gmail.com)
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Abstract

In urban areas, Global Positioning System (GPS) accuracy deteriorates due to signal degradation and multipath effects. To provide accurate and robust navigation in such GPS-denied environments, multi-sensor integrated navigation systems are developed. This paper introduces a 3D multi-sensor navigation system that integrates inertial sensors, odometry and GPS for land-vehicle navigation. A new error model is developed and an efficient loosely coupled closed-loop Kalman Filter (Extended KF or EKF) integration scheme is proposed. In this EKF-based integration scheme, the inertial/odometry navigation output is continuously corrected by EKF-estimated errors, which keeps the errors within acceptable linearization ranges which improves the prediction accuracy of the linearized dynamic error model. Consequently, the overall performance of the integrated system is improved. Real road experiments and comparison with earlier works have demonstrated a more reliable performance during GPS signal degradation and accurate estimation of inertial sensor errors (biases) have led to a more sustainable performance reliability during long GPS complete outages.

Keywords

GPSINSOdometryExtended Kalman Filter
Type
Research Article
Information
The Journal of Navigation , Volume 67 , Issue 4 , July 2014 , pp. 651 - 671
Copyright
Copyright © The Royal Institute of Navigation 2014 

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References

REFERENCES

Abrougui, K., Boukerche, A. and Pazzi, R. (2010). Context-aware and location-based service discovery protocol for Vehicular Networks: Proof of correctness. 858–865.Google Scholar
Arulampalam, M. S., Maskell, S., Gordon, N. and Clapp, T. (2002). A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Transactions on Signal Processing, 50(2), 174–188.Google Scholar
Atia, M. M., Georgy, J., Korenberg, M. and Noureldin, A. (2010). Real Time Implementation of Mixture Particle Filter for 3D RISS/GPS Integrated Navigation Solution. Electronic Letters, 46(15), 1083–1084.Google Scholar
Britting, K. R. (2010). Inertial Navigation Systems Analysis. Artech House, Inc.Google Scholar
CarChip. (2011). CarChip OBDII-Based Vehicle Data Logger and Software. [Online] Available at: http://www.davisnet.com/product_documents/drive/spec_sheets/8211-21-25_carchip_specsB.pdf [Accessed 16 4 2010].Google Scholar
Cossaboom, M., Georgy, J. and Karamat, T. (2012). Augmented Kalman Filter and Map Matching for 3D RISS/GPS Integration for Land Vehicles. International Journal of Navigation and Observation.Google Scholar
El-Rabbany, A. (2006). Introduction to GPS: The Global Positioning System. Artec House Inc.Google Scholar
El-Sheimy, N., Hou, A. and Niu, X. (2007). Analysis and Modeling of Inertial Sensors Using Allan Variance. IEEE Transactions on Instrumentation and Measurement, 57(1), 40–149.Google Scholar
Farrell, A. (2008). Aided Navigation, GPS with High-Rate Sensors. McGraw Hill.Google Scholar
Faulkner, N., Cooper, S. and Jeary, P. (2002). Integrated MEMS/GPS navigation systems. IEEE Position Location and Navigation Symposium, 306–313.CrossRefGoogle Scholar
Georgy, J., Noureldin, A., Korenberg, M. and Bayoumi, M. (2010). Low-Cost Three-Dimensional Navigation Solution for RISS/GPS Integration Using Mixture Particle Filter. IEEE Transaction on Vehicular Technology, 59(2), 599–615.CrossRefGoogle Scholar
Hwang, R. B. A. P. (1997). Introduction to random signals and applied Kalman filtering. John Wiley and Sons, New York.Google Scholar
IMU300. (2011). Crossbow Technology Inc, IMU300—6DOF Inertial Measurement.Google Scholar
Iqbal, U., Karamat, T. B. and Okou, A. F. (2009). Experimental Results on an Integrated GPS and Multisensors System for Land Vehicle Positioning. International Journal of Navigation and Observations.Google Scholar
Iqbal, U., Okou, A. F. and Noureldin, A. (2008). An Integrated Reduced Inertial Sensor System – RISS/GPS for Land Vehicle. Monterey, California, USA, s.n., 912–922.Google Scholar
Karamat, T., Georgy, J. and Iqbal, U. (2009). A Tightly-Coupled Reduced Multi-Sensor System for Urban Navigation. ION GNSS, Savannah, GA, USA, 582–592.Google Scholar
Mannings, R. (2008). Ubiquitous Positioning. Mobile Communication Series, Artech House.Google Scholar
Ming, C. and Yuming, S. (2006). Agent Based Intelligent Transportation Management System. Proceedings of the 6th International Conference on ITS Telecommunications, Chengdu, 190–193.Google Scholar
Misra, P. and Enge, P. (2011). Global Positioning System, Signals, Measurements, and Performance. Ganga-Jamuna Press.Google Scholar
Novatel. (2010). SPAN Technology System User Manual OM-20000062. http://www.novatel.com/Documents/Manuals/om-20000062.pdf. Accessed 16 April 2010.Google Scholar
Novatel. (2013). SPAN Technology System Characteristics and Performance. http://www.novatel.com/assets/Documents/Papers/SPAN_Performance.pdf. Accessed 11 November 2013.Google Scholar
Titterton, D. H. and Weston, J. L. (2004). Strapdown Inertial Navigation Technology. The Institution of Electrical Engineers, 2nd Edition.Google Scholar
Wang, Y. (2012). Position estimation using extended kalman filter and RTS-smoother in a GPS receiver. 5th International Congress on Image and Signal Processing, Chongqing. 1718–1721.Google Scholar