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.

Ground-based assets traditionally aid space vehicle navigation, but the need for autonomy is steadily growing to meet the demands of future deep-space exploration. This paper proposes a customized Trajectory-Aware Extended Kalman Filter (TA-EKF) architecture, which conforms to the kinematic approach for OD based on GNSS. Challenges at high altitudes, such as reduced GNSS signal availability and poor geometry, necessitate advanced filtering architectures leveraging external aiding data. When the receiver is not expected to interface with on-board guidance and control subsystems, aiding observations—in the form of a pre-mission planned spacecraft trajectory—allow to pursue precise and accurate OD only relying on GNSS measurements. Two alternative OAEKF designs are formulated, which foresee observation-domain and state-domain integration of aiding observations, respectively. While the former design acts directly on the filter posterior, the latter aims to overcome deficiencies in the state prediction owing to misspecified process dynamics. The feasibility of using terrestrial GNSS signals in MTO is thus demonstrated against aiding observation errors and mismodeling. The developed OAEKF models are thoroughly assessed via extensive MC analyses, comparing their OD performance against a standalone EKF solution in a dedicated constellation simulator and mission planner.

Positioning based on Global Navigation Satellite Systems (GNSSs) in urban environments always suffers from multipath and Non-Line-of-Sight (NLoS) effects. In such conditions, the GNSS pseudorange measurements can be affected by biases disrupting the GNSS-based applications. Many efforts have been devoted to detecting and mitigating the effects of multipath/NLoS, but the identification and classification of such events are still challenging. This research proposes a method for the post-processing estimation of pseudorange biases resulting from multipath/NLoS effects. Providing estimated pseudorange biases due to multipath/NLoS effects serves two main purposes. Firstly, machine learning-based techniques can leverage accurately estimated pseudorange biases as training data to detect and mitigate multipath/NLoS effects. Secondly, these accurately estimated pseudorange biases can serve as a benchmark for evaluating the effectiveness of the methods proposed to detect multipath/NLoS effects. The estimation is achieved by extracting the multipath/NLoS biases from pseudoranges using a clustering algorithm named Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The performance is demonstrated using two real-world data collections in multipath/NLoS scenarios for both static and dynamic conditions. Since there is no ground truth for the pseudorange biases due to the multipath/NLoS scenarios, the proposed method is validated based on the positioning performance. Positioning solutions are computed by subtracting the estimated biases from the raw pseudoranges and comparing them to the ground truth.

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%.

This letter presents a methodology to reduce the computational burden of Particle Filters (PF) used to achieve a target state estimation accuracy in Bayesian tracking problems. A strategy, named Multiple Weighting (MW), that exploits information diversity of the input measurements for an efficient weighting of the particles is introduced. This study shows that, by relying on the prior knowledge of the state-observations relationships, it is possible to achieve a significant reduction in the number of samples required to obtain any target accuracy. An experimental assessment is provided with an application to precise positioning estimation in Global Navigation Satellite System (GNSS) receivers.