For GPS medium-long baseline real-time kinematic (RTK) positioning, the troposphere parameter is introduced along with coordinates, and the model is ill-conditioned due to its strong correlation with the height parameter. For BeiDou Navigation Satellite System (BDS), additional difficulties occur due to its special satellite constellation. In fact, relative zenith troposphere delay (RZTD) derived from high-precision empirical zenith troposphere models can be introduced. Thus, the model strength can be improved, which is also called the RZTD-constrained RTK model. In this contribution, we first analyze the factors affecting the precision of BDS medium-long baseline RTK; thereafter, 15 baselines ranging from 38 km to 167 km in different troposphere conditions are processed to assess the performance of RZTD-constrained RTK. Results show that the troposphere parameter is difficult to distinguish from the height component, even with long time filtering for BDS-only RTK. Due to the lack of variation in geometry for the BDS geostationary Earth orbit satellite, the long convergence time of ambiguity parameters may reduce the height precision of GPS/BDS-combined RTK in the initial period. When the RZTD-constrained model was used in BDS and GPS/BDS-combined situations compared with the traditional RTK, the standard deviation of the height component for the fixed solution was reduced by 52.4% and 34.0%, respectively.

Advanced driver-assistance system (ADAS) and high levels of autonomy for vehicular applications require reliable and high precision pose information for their functioning. Pose estimation comprises solving the localization and orientation problems for a rigid body in a three-dimensional space. In outdoor scenarios, the fusion of Global Navigation Satellite Systems (GNSS) and inertial data in high-end receivers constitutes the baseline for ground truth localization solutions, such as Real-Time Kinematic (RTK) or Precise Point Positioning (PPP). These techniques present two main disadvantages, namely the inability to provide absolute orientation information and the lack of observations redundancy in urban scenarios. This paper presents Array PPP-RTK, a recursive three-dimensional pose estimation technique which fuses inertial and multi-antenna GNSS measurements to provide centimeters and sub-degree precision for positioning and attitude estimates, respectively. The core filter is based on adapting the well-known Extended Kalman Filter (EKF), such that it deals with parameters belonging to the SO(3) and GNSS integer ambiguity groups. The Array PPP-RTK observational model is also derived, based on the combination of carrier phase measurements over multiple antennas along with State Space Representation (SSR) GNSS corrections. The performance assessment is based on the real data collected on an inland waterway scenario. The results demonstrate that a high precision solution is available 99.5% of the time, with a horizontal precision of around 6 cm and heading precision of 0.9 degrees. Despite the satellite occlusion after bridge passing, it is shown that Array PPP-RTK recovers high accurate estimates in less than ten seconds.

With the emergence of BeiDou and Galileo as well as the modernization of GPS and GLONASS, more available satellites and signals enhance the capability of Global Navigation Satellite Systems (GNSS) to monitor the ionosphere. However, currently the International GNSS Service (IGS) Ionosphere Associate Analysis Centers (IAACs) just use GPS and GLONASS dual-frequency observations in ionosphere estimation. To better determine the global ionosphere, we used multi-frequency, multi-constellation GNSS observations and a priori International Reference Ionosphere (IRI) to model the ionosphere. The newly estimated ionosphere was represented by a spherical harmonic expansion function with degree and order of 15 in a solar-geomagnetic frame. By collecting more than 300 stations with a global distribution, we processed and analysed two years of data. The estimated ionospheric results were compared with those of IAACs, and the averaged Root Mean Squares (RMS) of Total Electron Content (TEC) differences for different solutions did not exceed 3 TEC Unit (TECU). Through validation by satellite altimetry, it was suggested that the newly established ionosphere had a higher precision than the IGS products. Moreover, compared with IGS ionospheric products, the newly established ionosphere showed a more accurate response to the ionosphere disturbances during the geomagnetic storms.

Traditionally, the ionosphere determination just uses American Global Positioning System and Russian GLObal NAvigation Satellite System dual‐frequency data and has a low precision particularly on oceans. With the rapid development of Chinese BeiDou and European Galileo systems, they are playing an increasingly important role for modeling global ionosphere. Meanwhile, satellite altimetry provides valuable and precise ionosphere delay over the oceans. Through introducing priori ionosphere values from an advanced empirical ionosphere model, combining the advantages of Global Navigation Satellite System (GNSS) and satellite altimetry technologies, the precision of global ionosphere estimation can be further improved. To assess the improvement, we collect satellite altimetry data from Jason‐2/3 and more than 300 global GNSS stations, the data are processed in 2014 and 2018 when the Sun is in a high and a low activity conditions. The results suggest that the ionosphere determination based on multitechnique fusion in a solar‐geomagnetic reference frame is well suitable to represent the ionosphere and its structure. The determined ionosphere achieves a better global consistency, and its formal accuracy is significantly reduced. By comparing with the International GNSS Service products, evaluating by satellite altimetry measurements, and independently validating with ionosonde techniques, it is proved that the ionosphere results are further improved through employing additional available data, especially for the ionosphere over the oceans.

Site-dependent effects are now the key factors that restrict the high accuracy applications of Global Navigation Satellite System (GNSS) technology, such as deformation monitoring. To reduce the effects of non-line-of-sight (NLOS) signal and multipath, methods and models applied to both of the function model and stochastic model of least-squares (LS) have been proposed. However, the existing methods and models may not be convenient to use and not be appropriate to all GNSS satellites. In this study, the SNR features of GPS and GLONASS are analyzed first, and a refined SNR based stochastic model is proposed, in which the links between carrier phase precision and SNR observation have been reasonably established. Compared with the existing models, the refined model in this paper could be used in real-time and the carrier phase precision could be reasonably shown with the SNR data. More importantly, it is applicable to all GNSS satellite systems. Based on this model, the site observation environment can be assessed in advance to show the obstruction area. With a bridge deformation monitoring platform, the performance of this model was tested in the aspect of integer ambiguity resolution and data processing. The results show that, compared with the existing stochastic models, this model could have the highest integer ambiguity resolution success rate and the lowest noise level in the data processing time series with obvious obstruction beside the site.

Real-time Kinematic (RTK) positioning provides centimeter-level positioning accuracy within several seconds, but it has to rely on a nearby base station. Although Precise Point Positioning (PPP) supplies precise positions with one receiver, its convergence time takes several tens of minutes, which makes PPP not well suited for real-time kinematic applications where a rapid convergence is required. PPP-RTK integrates the benefits of PPP and RTK, which actually is PPP augmented by a ground GNSS network. The satellite orbit, clock offsets, signal biases, ionospheric and tropospheric corrections are determined based on this GNSS network, modeled as state space information and transmitted to PPP users. By applying these State Space Representation (SSR) corrections, a real-time kinematic PPP-RTK approach is developed and implemented, which can instantly resolve the ambiguities to integers and realize rapid convergence. In a static scenario, it realized an instant ambiguity resolution and a rapid convergence within 2 s in more than 90% of 120 hourly sessions. The PPP-RTK has been applied and evaluated in a kinematic scenario on the highway. The horizontal positioning errors are almost lower than 0.1 m except for the time of passing through bridges. After passing bridges, the PPP-RTK successfully resolved ambiguities within 2 s in 90.6% of the cases and achieved convergence in horizontal within 5 s in more than 90% of the cases. The PPP-RTK with a precision of 0.1 m and rapid convergence of several seconds benefits the precise navigation of automobile on the highway, which will support the development of autonomous driving in future.