Enhanced EKF-Based Time Calibration for GNSS/UWB Tight Integration

Abstract

Tight integration of low-cost ultrawideband (UWB) ranging sensors with mass-market Global Navigation Satellite System (GNSS) receivers is gaining attention as a high-accuracy positioning strategy for consumer applications dealing with challenging environments. However, due to independent clocks embedded in commercial-off-the-shelf (COTS) chipsets, the time-scales associated with sensor measurements are misaligned, leading to inconsistent data fusion. Centralized, recursive filtering architectures can compensate for this offset and achieve accurate state estimation. In line with this, a GNSS/UWB tight integration scheme based on an extended Kalman filter (EKF) is developed that performs online time calibration of the sensors’ measurements by recursively modeling the GNSS/UWB time-offset as an additional unknown in the system state-space model. Furthermore, a double-update filtering model is proposed that embeds optimizations for the adaptive weighting of UWB measurements. Simulation results show that the double-update EKF algorithm can achieve a horizontal positioning accuracy gain of 41.60% over a plain EKF integration with uncalibrated time-offset and of 15.43% over the EKF with naive time-offset calibration. Moreover, a real-world experimental assessment demonstrates improved root-mean-square error (RMSE) performance of 57.58% and 31.03%, respectively.