Modeling the Stochastic Drift of a MEMS-Based Gyroscope in Gyro/Odometer/GPS Integrated Navigation

• Published in: IEEE transactions on intelligent transportation systems Vol. 11; no. 4; pp. 856 - 872
• Main Authors: Georgy, Jacques, Noureldin, Aboelmagd, Korenberg, Michael J, Bayoumi, Mohamed M
• Format: Journal Article
• Language: English
• Published: Piscataway, NJ IEEE 01.12.2010; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Autoregressive (AR) model; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Control theory. Systems; Exact sciences and technology; Filtering; Global Positioning System; Global Positioning System (GPS); Gyroscopes; inertial sensors; Kalman filter (KF); land vehicle navigation; Land vehicles; Measurements common to several branches of physics and astronomy; Metrology, measurements and laboratory procedures; Modelling and identification; Navigation; Nonlinear systems; parallel cascade identification (PCI); particle filter (PF); Physics; Sensor phenomena and characterization; Software; Stochastic models; Stochastic processes; Stochastic resonance; Studies; Vehicle dynamics; Velocity, acceleration and rotation; Positioning; Autoregressive (AR) model; Global Positioning System (GPS); Kalman filter (KF); Nonlinear filters; Non linear control; Autoregressive model; land vehicle navigation; Covariance matrices; Monte Carlo methods; particle filter (PF); Modelling; System identification; Kalman filters; Localization; parallel cascade identification (PCI); Nonlinear systems; Multisensor; Probabilistic approach; Cascade control; inertial sensors; Odometer; Interrupts; Nonlinear estimation; Kriging; Non linear filtering; Global Positioning System; Non linear model; Inertial navigation; Mechanical system; Position measurement; Distance measurement; Sensor array
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2010.2052805

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.