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International Journal of Navigation and Observation
Volume 2012 (2012), Article ID 576807, 16 pages
http://dx.doi.org/10.1155/2012/576807
Research Article

Augmented Kalman Filter and Map Matching for 3D RISS/GPS Integration for Land Vehicles

1Navigation and Instrumentation Research Group (NavINST), Electrical and Computer Engineering Department, Royal Military College of Canada, Kingston, ON, Canada K7K 7B4
2Trusted Positioning Inc., Calgary, AB, Canada T2L 2K7
3Navigation and Instrumentation Research Group (NavINST), Electrical and Computer Engineering Department, Queen’s University, Kingston, ON, Canada K7L 3N6

Received 1 July 2011; Revised 1 October 2011; Accepted 28 October 2011

Academic Editor: Olivier Julien

Copyright © 2012 Matthew Cossaboom et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Owing to their complimentary characteristics, global positioning system (GPS) and inertial navigation system (INS) are integrated, traditionally through Kalman filter (KF), to obtain improved navigational solution. To reduce the overall cost of the system, microelectromechanical system- (MEMS-) based INS is utilized. One of the approaches is to reduce the number of low-cost inertial sensors, decreasing their error contribution which leads to a reduced inertial sensor system (RISS). This paper uses KF to integrate GPS and 3D RISS in a loosely coupled fashion to enhance navigational solution while further improvement is achieved by augmenting it with map matching (MM). The 3D RISS consists of only one gyroscope and two accelerometers along with the vehicle’s built-in odometer. MM limits the error growth during GPS outages by restricting the predicted positions to the road networks. The performance of proposed method is compared with KF-only 3D RISS/GPS integration to demonstrate the efficacy of the proposed technique.