Tight integration of Global Navigation Satellite System (GNSS) and Ultra-Wide Band (UWB) is growing popularity as a positioning strategy relying on mass-market devices for applications dealing with localisation and tracking of agents in challenging environments. However, due to independent clocks of Commercial off-the-shelf (COTS) devices, the offset between the time-scales with respect to which measurements are time tagged must be mitigated to achieve accurate state-estimation via centralised, recursive filtering architectures. In this paper, it is analysed a GNSS/UWB Tightly-Coupled (TC) scheme based on an Extended Kalman Filter (EKF), and the integration of asynchronous GNSS/UWB measurements is investigated to highlight how it can induce state-estimation errors under different receiver kinematics. GNSS/UWB time calibration is addressed as a filtering problem, for which an EKF-based framework is developed to recursively model the GNSS/UWB time-offset as a further unknown in the system state-space model. Moreover, a double-update filtering model is proposed with embedded optimisations for the adaptive tuning of UWB ranging statistics. Experimental results with simulated data show that the double-update EKF algorithm can achieve a horizontal positioning accuracy gain 41.60 % over a plain EKF integration with uncalibrated time-offset and of 15.43 % over the EKF with naive time-offset calibration.

Tight integration of Global Navigation Satellite System (GNSS) and Ultra-Wide Band (UWB) is growing popularity as a positioning strategy relying on mass-market devices for applications dealing with localisation and tracking of agents in challenging environments. However, due to independent clocks of Commercial off-the-shelf (COTS) devices, the offset between the time-scales with respect to which measurements are time tagged must be mitigated to achieve accurate state-estimation via centralised, recursive filtering architectures. In this paper, it is analysed a GNSS/UWB Tightly-Coupled (TC) scheme based on an Extended Kalman Filter (EKF), and the integration of asynchronous GNSS/UWB measurements is investigated to highlight how it can induce state-estimation errors under different receiver kinematics. GNSS/UWB time calibration is addressed as a filtering problem, for which an EKF-based framework is developed to recursively model the GNSS/UWB time-offset as a further unknown in the system state-space model. Moreover, a double-update filtering model is proposed with embedded optimisations for the adaptive tuning of UWB ranging statistics. Experimental results with simulated data show that the double-update EKF algorithm can achieve a horizontal positioning accuracy gain 41.60 % over a plain EKF integration with uncalibrated time-offset and of 15.43 % over the EKF with naive time-offset calibration.