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Journal of Sensors
Volume 2016, Article ID 5365983, 16 pages
Research Article

A New Technique for Integrating MEMS-Based Low-Cost IMU and GPS in Vehicular Navigation

1LASSENA Laboratory, École de Technologie Supérieure, 1100 Notre-Dame Street West, Montreal, QC, Canada H3C 1K3
2Marine Navigation Research Institute, College of Automation, Harbin Engineering University, Harbin 150001, China
3LIV Laboratory, Electrical and Computer Engineering Department, Université de Sherbrooke, Sherbrooke, QC, Canada J1K 2R1

Received 20 November 2015; Revised 4 April 2016; Accepted 28 April 2016

Academic Editor: Maan E. El Najjar

Copyright © 2016 Neda Navidi 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.


In providing acceptable navigational solutions, Location-Based Services (LBS) in land navigation rely mostly on integration of Global Positioning System (GPS) and Inertial Navigation System (INS) measurements for accuracy and robustness. The GPS/INS integrated system can provide better land-navigation solutions than the ones any standalone system can provide. Low-cost Inertial Measurement Units (IMUs), based on Microelectromechanical Systems (MEMS) technology, revolutionized the land-navigation system by virtue of their low-cost miniaturization and widespread availability. However, their accuracy is strongly affected by their inherent systematic and stochastic errors, which depend mainly on environmental conditions. The environmental noise and nonlinearities prevent obtaining optimal localization estimates in Land Vehicular Navigation (LVN) while using traditional Kalman Filters (KF). The main goal of this paper is to effectively eliminate stochastic errors of MEMS-based IMUs. The proposed solution is divided into two main components: (1) improving noise cancellation, using advanced stochastic error models in MEMS-based IMUs based on combined Autoregressive Processes (ARP) and first-order Gauss-Markov Process (1GMP), and (2) modeling the low-cost GPS/INS integration, using a hybrid Fuzzy Inference System (FIS) and Second-Order Extended Kalman Filter (SOEKF). The results obtained show that the proposed methods perform better than the traditional techniques do in different stochastic and dynamic situations.