A GNSS Quality Control Based GNSS/IMU Integrated Navigation Algorithm in Urban Environments

Global Navigation Satellite Systems (GNSS) and Inertial Measurement Units (IMU) are often integrated to provide an essential Positioning, Navigation and Timing (PNT) solution for vehicles. Nevertheless, the accuracy and reliability of this PNT solution are degraded greatly in complex urban environments due to the deterioration of GNSS measurement quality. To deal with this issue, we here propose a two-stage GNSS quality control algorithm for integrated GNSS/IMU systems. In stage one, a novel Fault Detection and Exclusion (FDE) scheme is applied at each epoch to calculate fault detection statistics based on two datasets: 1) the universal set of the pseudorange and 2) the subsets of the observed pseudorange based on the single-fault hypothesis. In stage two, an adaptive Kalman filter is applied to the GNSS/IMU integration with the parameters in the measurement noise covariance matrix optimized according to the changes in the satellite elevation angle. In addition, the Non-Holonomic Constraint (NHC) is applied to suppress the accumulation of positioning errors. Field test results show that the proposed algorithm can improve the horizontal and 3D positioning accuracy by 32.39% and 31.68%, respectively. It can also improve the accuracy of the velocity and heading angle by more than 25% compared to the traditional GNSS/IMU integrated algorithm.