An integrated low cost reduced inertial sensor system/GPS for land vehicle applications

Global position system (GPS) is presently widely used in land vehicles to continuously provide positioning information. However, in urban canyons, the GPS satellite signal is usually blocked and there is an interruption in the positioning information provided to the driver. To obtain positioning solution during GPS outages, it was suggested to augment the GPS with an inertial measurement unit (IMU) with information from both systems fused with optimal estimation criterion (usually based on Kalman filtering). However, the utilization of full IMU in land vehicle would be quite expensive even with the use of the low cost micro-electro-mechanical-system (MEMS)-based sensors. Hence, research efforts have been recently conducted to investigate the applicability of reduced number of inertial sensors inside an IMU and examine the influence of such technology on the overall positioning accuracy.;This thesis explores a reduced inertial sensor system (RISS) involving single-axis gyroscope and two-axis accelerometers together with an odometer to provide full navigation solution in denied GPS environments. Furthermore a Kalamn filter (KF) model is utilized to predict the position errors of the proposed RISS. The KF fuses both GPS and RISS using error model developed in this study. With the assumption that the vehicle mostly stay in the horizontal plane, the vehicle speed obtained from the odometer measurements are used together with the heading information obtained from the gyroscope to determine the velocities along the East and North directions. Consequently, the vehicles' longitude and latitude are determined. The errors will be determined by a KF relying on dynamic error model of RISS position, velocity and azimuth errors as well as stochastic error models for both the gyroscope and odometer errors. In case of a GPS outage, the RISS together with the KF error model should be capable of providing positioning information.;This research also addresses the computation of the roll and pitch angles that are usually determined using those two gyroscopes eliminated in the RISS. In this research, two accelerometers (pointing towards the forward and the transverse directions of the vehicle) together with a reliable model for the Earth gravity are used for this purpose. The vehicle acceleration (derived from the odometer measurements) is removed from the accelerometers measurements before computing the roll and pitch.;In general, this thesis demonstrates a low cost navigation solution that can efficiently work, in real-time, in denied GPS environments. This research discusses and analyzes the merits and limitations of the proposed RISS and its integration with GPS using KF module. The performance of the proposed method is examined by conducting several road tests in land vehicles. Both low cost MEMS and tactical grade inertial sensors were used for the RISS KF module and integrated with GPS. The results from each sensor system i.e. Tactical and MEMS for several trajectories are discussed in this study.