A key limitation for precise orbit determination of BeiDou satellites, particularly for satellites in geostationary orbit (GEO), is the relative weak geometry of ground stations. Fortunately, data from a low earth orbiting satellite with an onboard GNSS receiver can improve the geometry of GNSS orbit determination compared to using only ground data. The Chinese FengYun-3C (FY3C) satellite carries the GNSS Occultation Sounder equipment with both dual-frequency GPS (L1 and L2) and BeiDou (B1 and B2) tracking capacity. The satellite-induced variations in pseudoranges have been estimated from multipath observables using an elevation-dependent piece-wise linear model, in which the constant biases, i.e., ambiguities and hardware delays, have been removed. For IGSO and MEO satellites, these variations can be seen in onboard B1 and B2 code measurements with elevation above 40°. For GEO satellites, a different behavior has been observed for these signals. The GEO B2 pseudoranges variations are similar to those of IGSO satellites, but no elevation-dependent variations have been identified for GEO B1. A possible cause is contamination of the larger noise in GEO B1 signals. Two sets of precise orbits were determined for FY3C in March 2015 using onboard GPS-only data and onboard BeiDou-only data, respectively. The 3D RMS (Root Mean Square) of overlapping orbit differences (OODs) is 2.3 cm for GPS-only solution. The 3D RMS of orbit differences between BeiDou-only and GPS-only solutions is 15.8 cm. Also, precise orbits and clocks for BeiDou satellites were determined based on 97 global (termed GN) or 15 regional (termed RN) ground stations. Furthermore, also using FY3C onboard BeiDou data, two additional sets of BeiDou orbit and clock products are determined with the data from global (termed GW) or regional (termed RW) stations. In general, the OODs decrease for BeiDou satellites, particularly for GEO satellites, when the FY3C onboard BeiDou data are added. The 3D OODs reductions are 10.0 and 291.2 cm for GW and RW GEO solution with respect to GN and RN solution, respectively. Since the OODs in the along-track direction dominate the OODs reduction, no improvement has been observed by satellite laser ranging, which mainly validates the accuracy of the radial orbital component. With the GW BeiDou orbit and clock products, the FY3C orbits determined with onboard BeiDou-only data also show improvement in comparison with those determined with BeiDou GN products.

The benefits of an increased number of global navigation satellite systems (GNSS) in space have been confirmed for the robustness and convergence time of standard precise point positioning (PPP) solutions, as well as improved accuracy when (most of) the ambiguities are fixed. Yet, it is still worthwhile to investigate fast and high-precision GNSS parameter estimation to meet user needs. This contribution focuses on integer ambiguity resolution-enabled Precise Point Positioning (PPP-RTK) in the use of the observations from four global navigation systems, i.e., GPS (Global Positioning System), Galileo (European Global Navigation Satellite System), BDS (Chinese BeiDou Navigation Satellite System), and GLONASS (Global’naya Navigatsionnaya Sputnikova Sistema). An undifferenced and uncombined PPP-RTK model is implemented for which the satellite clock and phase bias corrections are computed from the data processing of a group of stations in a network and then provided to users to help them achieve integer ambiguity resolution on a single receiver by calibrating the satellite phase biases. The dataset is recorded in a local area of the GNSS network of the Netherlands, in which 12 stations are regarded as the reference to generate the corresponding corrections and 21 as the users to assess the performance of the multi-GNSS PPP-RTK in both kinematic and static positioning mode. The results show that the root-mean-square (RMS) errors of the ambiguity float solutions can achieve the same accuracy level of the ambiguity fixed solutions after convergence. The combined GNSS cases, on the contrary, reduce the horizontal RMS of GPS alone with 2 cm level to GPS + Galileo/GPS + Galileo + BDS/GPS + Galileo + BDS + GLONASS with 1 cm level. The convergence time benefits from both multi-GNSS and fixing ambiguities, and the performances of the ambiguity fixed solution are comparable to those of the multi-GNSS ambiguity float solutions. For instance, the convergence time of GPS alone ambiguity fixed solutions to achieve 10 cm three-dimensional (3D) positioning accuracy is 39.5 min, while it is 37 min for GPS + Galileo ambiguity float solutions; moreover, with the same criterion, the convergence time of GE ambiguity fixed solutions is 19 min, which is better than GPS + Galileo + BDS + GLONASS ambiguity float solutions with 28.5 min. The experiments indicate that GPS alone occasionally suffers from a wrong fixing problem; however, this problem does not exist in the combined systems. Finally, integer ambiguity resolution is still necessary for multi-GNSS in the case of fast achieving very-high-accuracy positioning, e.g., sub-centimeter level.

The water vapor content in the atmosphere can be reconstructed using the all-weather condition troposphere tomography technique. In common troposphere tomography, the water vapor of each voxel is represented by an unknown parameter. This means that when the desired spatial resolution is high or study area is large, there will be a huge number of unknown parameters in the problem that need to be solved. This defect can reduce the accuracy of troposphere tomography results. In order to overcome this problem, an optimal voxel-based troposphere tomography using the Weather Research and Forecasting (WRF) model is proposed. The new approach reduces the number of unknown parameters, the number of empty voxels and the role of constraints required to enhance the spatial resolution of tomography results in required areas. Furthermore, the effect of considering the topography of the study area in the tomography model is examined. The obtained water vapor is validated by radiosonde observations and Global Positioning System (GPS) positioning results. Comparison of the results with the radiosonde observations shows that using the WRF model outputs and topography of the area can reduce the Root Mean Square Error (RMSE) by 0.803 gr/m3. Validation using positioning shows that in wet weather conditions, the WRF model outputs and topography reduce the RMSE of the east, north and up components by about 17.42, 10.46 and 20.03 mm, which are equivalent to 46.01%, 35.78% and 53.93%, respectively.

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