Merits and limitations of using fuzzy inference system for temporal integration of INS/GPS in vehicular navigation

  • Authors:

  • Rashad Sharaf;Mahmoud Reda Taha;Mohammed Tarbouchi;Aboelmagd Noureldin

  • Affiliations:

  • Royal Military College of Canada, Department of Electrical and Computer Engineering, Kingston, ON, Canada;The University of New Mexico, Department of Civil Engineering, MSC01 10701, 87131-0001, Albuquerque, NM, USA;Royal Military College of Canada, Department of Electrical and Computer Engineering, Kingston, ON, Canada;Royal Military College of Canada, Department of Electrical and Computer Engineering, Kingston, ON, Canada

  • Venue:
  • Soft Computing - A Fusion of Foundations, Methodologies and Applications
  • Year:
  • 2007

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Abstract

Most of the present vehicular navigation systems rely on global positioning system (GPS) combined with inertial navigation system (INS) for reliable determination of the vehicle position and heading. Integrating both systems provide several advantages and eliminate their individual shortcomings. Kalman filter (KF) has been widely used to fuse data from both systems. However, KF-based integration techniques suffer from several limitations related to its immunity to noise, observability and the necessity of accurate stochastic models of sensor random errors. This article investigates the potential use of adaptive neuro-fuzzy inference system (ANFIS) for temporal integration of INS/GPS in vehicular navigation. An ANFIS-based module named “P–δP” is designed, developed, implemented and tested for fusing INS and GPS position information. The fusion process aims at providing continuous correction of INS position to prevent its long-term growth using GPS position updates. In addition, it provides reliable prediction of the vehicle position during GPS outages. The P–δP module was examined using real navigation system data compromising an Ashtech Z12 GPS receiver and a Honeywell LRF-III INS. The proposed module proved to be successful as a modeless and platform independent module that does not require a priori knowledge of the navigation equipment utilized. Limitations of the ANFIS module are also discussed.