Due to their complementary characteristics, Global Positioning System (GPS) is usually integrated with standalone navigation devices like odometers and inertial measurement units (IMU). Recently, intensive research has focused on utilizing Micro-Electro-Mechanical-System (MEMS) grade inertial sensors in the integration because of their low cost. In this study, a reduced inertial sensor system (RISS) is considered. It comprises a MEMS grade single axis gyroscope, the vehicle built-in odometer, and two optional MEMS grade accelerometers. Estimation technique is needed to allow the data fusion of RISS and GPS. With adequate accuracy, Kalman filter (KF) fulfills this requirement if high-end inertial sensors are used. However, due to the inherent error characteristics of MEMS devices, MEMS-based RISS suffers from the non-stationary stochastic sensor errors and nonlinear inertial errors, which cannot be suppressed by KF alone. To solve the problem, Fast Orthogonal Search (FOS), a nonlinear system identification algorithm, is suggested in this research for modeling higher order RISS errors. FOS algorithm has the ability to figure out the system nonlinearity with a tolerance of arbitrary stochastic system noise. Its modeling results can then be used to predict the system dynamics. Motivated by the above merits, a KF/FOS module is proposed. By handling both linear and nonlinear RISS errors, this module targets substantial enhancement of positioning accuracy. To examine the effectiveness of the proposed technique, KF/FOS module is applied on RISS with GPS in a land vehicle for several road test trajectories. Its performance is compared to KF-only method, both assessed with respect to a high-end reference. To evaluate navigation algorithm in real-time vehicle application, a multi-sensor data logger is designed in this research to collect online RISS/GPS data. KF/FOS module is transplanted on an embedded digital signal processor as well. Both the off-line and online results confirm that KF/FOS module outperforms KF-only approach in positioning accuracy. They also demonstrate reliable real-time performance. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2012-01-22 01:26:11.477

Present land vehicle positioning and navigation relies mostly on the Global Positioning System (GPS). However, in urban canyons, tunnels, and other GPS-denied environments, the GPS positioning solution may be interrupted or suffer from deterioration in accuracy due to satellite signal blockage, poor satellite geometry or multipath effects. In order to achieve continuous positioning services, GPS is augmented with complementary systems capable of providing additional sources of positioning information, like inertial navigation systems (INS). Kalman filtering (KF) is traditionally used to provide integration of both INS and GPS utilizing linearized dynamic system and measurement models. Targeting low cost solution for land vehicles, Micro-Electro-Mechanical Systems (MEMS) based inertial sensors are used. Due to the inherent errors of MEMS inertial sensors and their stochastic nature, which is difficult to model, KF has limited capabilities in providing accurate positioning in challenging GPS environments. This research aims at developing reliable integrated navigation system capable of demonstrating accurate positioning during long periods of challenging GPS environments. Towards achieving this goal, Mixture Particle filtering (MPF) is suggested in this research as a nonlinear filtering technique for INS/GPS integration to accommodate arbitrary inertial sensor characteristics, motion dynamics and noise distributions. Since PF can accommodate nonlinear models, this research develops total-state nonlinear system and measurement models without any linearization, thus enabling reliable integrated navigation and mitigating one of the major drawbacks of KF. Exploiting the capabilities of PF, Parallel Cascade Identification (PCI), which is a nonlinear system identification technique, is used to obtain efficient stochastic models for inertial sensors instead of the currently utilized linear models, which are not adequate for MEMS-based sensors. Moreover, this research proposes a method to update the stochastic bias drift of inertial sensors from GPS data when the GPS signal is adequately received. Furthermore, a technique for automatic detection of GPS degraded performance is developed and led to improving the performance in urban canyons. The performance is examined using several road test experiments conducted in downtown cores to verify the adequacy and the benefits of the methods suggested. The results obtained demonstrate the superior performance of the proposed methods over conventional techniques. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2010-07-23 20:27:02.12