## 3 Results  
### Data and Experiments  
To verify the positioning performance of the PPP-RTK algorithm, a vehicle equipped with GNSS and INS measuring instruments was used to collect 78 min of on-board kinematic data in Wuhan on 28 May 2024. The continuously operating reference station (CORS) in Wuhan was utilized to generate atmospheric products and their accuracy information for the rover. In Figure 2, the left side of the panel shows the reference station network in the Wuhan region, comprising a total of five reference stations: HBA2, HBB0, HBF7, HBE0, and HBH8. The maximum distance between adjacent reference stations was 84 km, with an average distance of 62 km. This network was used to generate atmospheric information for the rover. To obtain the interpolated atmospheric accuracy information for the rover, HBA1 was also used to verify atmospheric accuracy. HBA1, a reference station closer to the user station, was utilized to calculate the precise atmospheric conditions at HBA1. This allowed the determination of the approximate accuracy of atmospheric interpolation within the network. At the same time, in order to verify the static stations' performance in terms of PPP-RTK, HBA1 was used for static experiments imitating kinematic solutions. The position of the reference station shown in the left panel was the kinematic starting point, and a reference station was set up at this position. The figure shows the measurement of the vehicle's kinematic trajectory, with a data collection time ranging from 06:57:15 to 09:02:12 GPST. The total length of the vehicle's journey was approximately 48 km. The vehicles passed through complex road conditions involving trees, tunnels, and elevated bridges. At locations A and B, as shown in the right-hand side of the figure, GNSS locking was lost due to the vehicle passing over the elevated bridge. The positioning performance of PPP-RTK was evaluated using high-precision GNSS data processing software, Novatel Waypoing 8.9, to calculate the smoothed RTK/INS tight combination solution as a reference. The algorithm implementation was based on POSGO (position based on graph optimization) software [35]. Table 1 details the strategy employed for the PPP-RTK positioning system. The TurboEdit algorithm [36] and LLI flag were used for data quality control. The final WUM products from the Wuhan University Analysis Center of International GNSS Service Multi-GNSS Experiments were used [37]. The extraction and validation of precise atmospheric products were performed via localization using an UDUC function model. The carrier phase noise at each frequency for all constellations was set to 2 cm with an empirical code-to-carrier ratio of 100:1 [38; 39]. To weaken the multipath effect of low-elevation observations, we adopted an elevation-dependent function for weighting, i.e., \((2\cdot\sin(ele))^{2}\) for satellites with elevations lower than 30\({}^{\circ}\) and 1.0 for those above 30 degrees. The mask elevation was set to 10 degrees. We employed PPP-AR to extract ionosphere and troposphere products from the reference stations, utilizing raw observations at the GPS L1/L2, Galileo E1/E5a, and BDS B1I/B3I frequencies, with a sampling interval of 5 s. The station coordinates remained fixed during the extraction of atmospheric products, and ambiguity resolution was accomplished based on the WUM final code and phase OSB. To eliminate receiver-related biases, single-difference ambiguities were formed before performing PPP-AR, and a cascading integer resolution method, similar to the approach used by [PERSON] [38], was employed. In order to improve the ambiguity fixing rate, partial ambiguity resolution (PAR) was applied, with detailed information available in the work of [PERSON] et al. [39]. The slant ionospheric parameters were treated as white noise, and only satellites with ambiguity resolution contributed to the estimation of ionospheric products. The ZWD parameter was modeled as a random walk, and only fixed epochs were used for ZWD extraction.