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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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