3D Grid-Based Resilient Pseudorange Error Prediction for Adaptive GNSS/IMU Integrated Navigation in Urban Areas

Non-line of sight (NLOS) and multipath are known to cause pseudorange measurement errors, leading to excessive positioning errors in challenging urban environments. Although GNSS and inertial measurement units (IMU) integrated system can enhance the positioning performance, the positioning accuracy is still constrained by the filter performance. The existing pseudorange correction algorithm employs simple measurement noise covariance adjusting strategy, which is unable to adapt the varying measurement noise in the complex urban environment. To solve this issue, a three-dimensional grid-based resilient pseudorange error prediction algorithm is proposed to adjust the measurement noise covariance R in Kalman filter (3D-RKF) in urban areas. The urban area is divided by the proposed 3D grid layout and the pseudorange errors are predicted by ensemble bagged regression tree (EBRT) grid by grid to achieve fine-scale pseudorange prediction. R is then updated by the proposed model reliability indicator (MRI)-based adaptive fusion strategy. Experimental results in complex urban areas prove the proposed algorithm can reach a 3D accuracy of 10.16 m, with an improvement of 52% compared to the EKF-based fusion, 33% compared to two-dimensional grid-based adaptive Kalman filter algorithm without MRI-based adaptive fusion strategy (2D-AKF) and 22% compared to 3D grid-based adaptive Kalman filter algorithm without MRI-based adaptive fusion strategy (3D-AKF), respectively.