Multi-Sensor Data Fusion for Vehicular Navigation Applications
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Global position system (GPS) is widely used in land vehicles but suffers deterioration in its accuracy in urban canyons; mostly due to satellite signal blockage and signal multipath. To obtain accurate, reliable, and continuous positioning solutions, GPS is usually augmented with inertial sensors, including accelerometers and gyroscopes to monitor both translational and rotational motions of a moving vehicle. Due to space and cost requirements, micro-electro-mechanical-system (MEMS) inertial sensors, which are typically inexpensive are presently utilized in land vehicles for various reasons and can be used for integration with GPS for navigation purposes. Kalman filtering (KF) usually used to performs this integration. However, the complex error characteristics of these MEMS based sensors lead to divergence of the positioning solution. Furthermore, the residual GPS pseudorange correlated errors are always ignored, thus reducing the GPS overall positioning accuracy. This thesis targets enhancing the performance of integrated MEMS based INS/GPS navigation systems through exploring new non-linear modelling approaches that can deal with the non-linear and correlated parts of INS and GPS errors. The research approach in this thesis relies on reduced inertial sensor systems (RISS) incorporating single axis gyroscope, vehicle odometer, and accelerometers is considered for the integration with GPS in one of two schemes; either loosely-coupled where GPS position and velocity are used for the integration or tightly-coupled where GPS pseudorange and pseudorange rates are utilized. A new method based on parallel cascade identification (PCI) is developed in this research to enhance the performance of KF by modelling azimuth errors for the RISS/GPS loosely-coupled integration scheme. In addition, PCI is also utilized for the modelling of residual GPS pseudorange correlated errors. This thesis develops a method to augment a PCI – based model of GPS pseudorange correlated errors to a tightly-coupled KF. In order to take full advantage of the PCI based models, this thesis explores the Particle filter (PF) as a non-linear integration scheme that is capable of accommodating the arbitrary sensor characteristics, motion dynamics, and noise distributions. The performance of the proposed methods is examined through several road test experiments in land vehicles involving different types of inertial sensors and GPS receivers.