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Tight integration of low-cost Ultra-Wide Band (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.

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.

In the last decades, the increasing complexity of the fusion of proprioceptive and exteroceptive sensors with Global Navigation Satellite System (GNSS) has motivated the exploration of Artificial Intelligence related strategies for the implementation of the navigation filters. In order to meet the strict requirements of accuracy and precision for Intelligent Transportation Systems (ITS) and Robotics, Bayesian inference algorithms are at the basis of current Positioning, Navigation, and Timing (PNT). Some scientific and technical contributions resort to Sequential Importance Resampling (SIR) Particle Filters (PF) to overcome the theoretical weaknesses of the more popular and efficient Kalman Filters (KFs) when the application relies on non-linear measurements models and non-Gaussian measurements errors. However, due to its higher computational burden, SIR PF is generally discarded. This paper presents a methodology named Multiple Weighting (MW) that reduces the computational burden of PF by considering the mutual information provided by the input measurements about the unknown state. An assessment of the proposed scheme is shown through an application to standalone GNSS estimation as a baseline of more complex multi-sensors, integrated solutions. By relying on the a-priori knowledge of the relationship between states and measurements, a change in the conventional PF routine allows performing a more efficient sampling of the posterior distribution. Results show that the proposed strategy can achieve any desired accuracy with a considerable reduction in the number of particles. Given a fixed and reasonable available computational effort, the proposed scheme allows for an accuracy improvement of the state estimate in the range of 20–40%.

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