UNIVERSITY OF CALGARY Accuracy Enhancement of Integrated MEMS-IMU/GPS Systems for Land Vehicular Navigation Applications

This research aims at enhancing the accuracy of land vehicular navigation systems by integrating GPS and Micro-Electro-Mechanical-System (MEMS) based inertial measurement units (IMU). This comprises improving the MEMS-based inertial output signals as well as investigating the limitations of a conventional Kalman Filtering (KF) solution for MEMS-IMU/GPS integration. These limitations are due to two main reasons. The first is that a KF suppresses the effect of inertial sensor noise using GPS-derived position and velocity as updates but within a limited band of frequency. The second reason is that a KF only works well under certain predefined dynamic models and convenient input data that fit these models, which are not attainable with the utilization of MEMS-based inertial technology. Therefore, if the GPS reference solutions are lost, the accuracy of standalone MEMS-IMU navigation will drastically degrade over time. The Wavelet Multi-Resolution Analysis (WMRA) technique is proposed in this thesis as an efficient pre-filter for MEMS-based inertial sensors outputs. Applying this prefiltering process successfully improves the sensors’ signal-to-noise ratios, removes shortterm errors mixed with motion dynamics, and provides more reliable data to the KFbased MEMS-INS/GPS integration module. The results of experimental validation show the effectiveness of the proposed WMRA method in improving the accuracy of KF estimated navigation states particularly position. Moreover, the Adaptive-Neuro-Fuzzyinference-system (ANFIS)-based algorithm is suggested and assessed to model the variations of the MEMS sensors’ performance characteristics with temperature. The