Modeling the stochastic drift of a MEMS-based gyroscope in gyro/odometer/GPS integrated navigation

  • Authors:

  • Jacques Georgy;Aboelmagd Noureldin;Michael J. Korenberg;Mohamed M. Bayoumi

  • Affiliations:

  • Department of Electrical and Computer Engineering, Queen's University, Kingston, ON, Canada and Department of Computer and Systems Engineering, Ain Shams University, Cairo, Egypt and Royal Militar ...;Department of Electrical and Computer Engineering, Queen's University, Kingston, ON, Canada and Department of Electrical and Computer Engineering, Royal Military College of Canada, Kingston, ON, C ...;Department of Electrical and Computer Engineering, Queen's University, Kingston, ON, Canada;Department of Electrical and Computer Engineering, Queen's University, Kingston, ON, Canada

  • Venue:
  • IEEE Transactions on Intelligent Transportation Systems
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

To have a continuous navigation solution that does not suffer from interruption, GPS is integrated with relative positioning techniques such as odometry and inertial navigation. Targeting a low-cost navigation solution for land vehicles, this paper uses a reduced multisensor system consisting of one microelectromechanical-system (MEMS)-based single-axis gyroscope used together with the vehicle's odometer, and the whole system is integrated with GPS. This system provides a 2-D navigation solution, which is adequate for land vehicles. The traditional technique for this multisensor integration problem is Kalman filtering (KF). Due to the inherent errors of MEMS inertial sensors and their stochastic nature, which is difficult to model, the KF with its linearized models has limited capabilities in providing accurate positioning. Particle filtering (PF) has recently been suggested as a nonlinear filtering technique to accommodate arbitrary inertial sensor characteristics, motion dynamics, and noise distributions. An enhanced version of PF is utilized in this paper and is called the Mixture PF. Since PF can accommodate nonlinear models, this paper uses total-state nonlinear system and measurement models. In addition, sophisticated models are used to model the stochastic drift of the MEMS-based gyroscope. A nonlinear system identification technique based on parallel cascade identification (PCI) is used to model this stochastic gyroscope drift. In this paper, the performance of the PCI model is compared with that of higher order autoregressive (AR) stochastic models. Such higher order models are difficult to use with KF since the size of the dynamic matrix and the error-covariance matrix becomes very large and complicates the KF operation. The performance of the proposed 2-D navigation solution using Mixture PF with both PCI and higher order AR models is examined by road-test trajectories in a land vehicle. The two proposed combinations are compared with four other 2-D solutions: a Mixture PF with the Gauss-Markov (GM) model for the gyro drift, a Mixture PF with only white Gaussian noise (WGN) for stochastic gyro errors, and two different KF solutions with GM model for the gyro drift. The experimental results show that the two proposed solutions outperform all the compared counterparts.