Pseudorange error prediction for adaptive tightly coupled GNSS/IMU navigation in urban areas

The integration of global navigation satellite systems (GNSS) and inertial measurement unit (IMU) with the Kalman filter is widely used to enhance the availability of positioning in urban areas for many intelligent transport system (ITS) applications. In the traditional Kalman filter, the GNSS measurement noise is fixed based on factors determined a priori, instead of reflecting the impact of the surrounding environment on the received GNSS signal. This has the effect of degrading position accuracy and the a posteriori quality indicators. To address this issue, we propose a new measurement noise covariance update scheme, with the adaptive indicator generated from pseudorange error prediction results, for a tightly coupled GNSS/IMU navigation system in urban areas. Specifically, the pseudorange errors are predicted by means of an ensemble bagged regression tree model accounting for signal strength, satellite elevation angle and coordinate information. The urban experimental results show that the proposed algorithm provides a 3D accuracy of 9.21 m, with an improvement of 55% and 15%, respectively, over the traditional fixed covariance extended Kalman filter (EKF)-based fusion and EKF-based fusion with pseudorange error correction.

Automated summary

This study proposes a new measurement noise covariance update scheme for a tightly coupled GNSS/IMU navigation system in urban areas. The scheme utilizes an adaptive indicator generated from pseudorange error prediction results to address the issue of fixed GNSS measurement noise. Experimental results show significant improvements in positioning accuracy compared to traditional methods.