Improved Land Vehicle Navigation and GPS Integer Ambiguity Resolution Using Enhanced Reduced-IMU/GPS Integration
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Land vehicle navigation is primarily dependent upon the Global Positioning System (GPS) which provides accurate navigation in open sky. However, in urban and rural canyons GPS accuracy degrades considerably. To help GPS in such scenarios, it is often integrated with inexpensive inertial sensors. Such sensors have complex stochastic errors which are difficult to mitigate. In the presence of speed measurements from land vehicle, a reduced number of inertial sensors can be used which improve performance and termed as the Reduced Inertial Sensor System (RISS). Existing low-cost RISS/GPS integrated algorithms have limited accuracy due to use of approximations in error models and employment of a Linearized Kalman Filter (LKF). This research developed an enhanced error model for RISS which was integrated with GPS using an Extended Kalman Filter (EKF) for improved navigation of land vehicles. The proposed system was tested on several road experiments and the results confirmed the sustainable performance of the system during long GPS outages. To further increase the accuracy, Differential GPS (DGPS) is employed where carrier phase measurements are typically used. This requires resolution of Integer Ambiguity (IA) that comes at computational and time expense. This research uses pseudorange measurements for DGPS which mitigate large biases due to atmospheric errors and obviate the resolution of IA. These measurements are integrated with the enhanced RISS to filter increased noise and help GPS during signal blockages. The performance of the proposed system was compared with two other algorithms employing undifferenced GPS measurements where atmospheric effects are mitigated using either the Klobuchar model or dual frequency receivers. The proposed system performed better than both the algorithms in positional accuracy, multipath and GPS outages. This research further explored the reduction of Time-to-Fix Ambiguities (TTFA) for land vehicle navigation. To reduce the TTFA through inertial aiding, previous research used high-end Inertial Measurement Units (IMUs). This research uses MEMS grade IMU by integrating the enhanced RISS with carrier phase measurements using EKF. This algorithm was also tested on three road trajectories and it was shown that this integration helps reduce the TTFA as compared to the GPS-only case when fewer satellites are visible.