## 6 Conclusions  
The interpolation model that was used for regional corrections is one of the key algorithms in a regional network ground-based enhanced positioning system. This paper proposes an SD ionospheric delay interpolation model for use with ionospheric corrections that are provided by the server in PPP-RTK technology through fixed ambiguity. CORS data from the United States were used together with a GPS as an example to analyze the interpolation accuracy of the proposed interpolation for four scenarios: a medium-scale reference network, a large-scale reference network, out-of-network users, and a network with a minimum of three reference stations. There were two typical scenarios of medium-scale and large-scale reference networks that were selected to analyze the PPP-RTK positioning performance of the model when constrained by atmospheric parameters. The PPP solution was reset every 5 min for the GPS and Galileo dual-system observation data. Each solution lasted for 5 min, and a total of 270 groups of data were analyzed. The conclusions that were drawn from the study results are as follows:  
(1) The accuracy of the interpolation model decreases as the distance between the reference stations increases. The RMS values of the SD ionospheric delay residuals of DSM for the 41-km and 98-km reference networks were 1.4 cm and 3.2 cm, respectively. The interpolation accuracy of DSM was better than that of DIM and USM in both scenarios. When the user station outside the network was 33 km away from the nearest reference station, the RMS of ionospheric residual using DSM was 2.2 cm, which is better than that for DIM and USM under the same conditions. Unlike DIM, DSM takes the variation in the ionospheric delay of each satellite pair in different directions into account fully, so the interpolation accuracy is better than that of DIM. A correlation was obtained using USM in the test, which resulted in the inaccurate interpolation of atmospheric delay in some epochs. The RMS and STD values for DSM were equal in the different scenarios that were considered, which suggests that there is no systematic error in the ionospheric delay residuals using the new model. (2) When there are three reference stations, the interpolation accuracies that were achieved with DIM and DSM were unchanged compared with those for four reference stations. This indicates that the server can still try to provide ionospheric corrections service under the condition of fewer reference stations, whereas USM cannot provide service due to the lack of enough reference stations. (3) Under the condition that the same empirical model is used for the mean square error of atmospheric delay, the TTFF of PPP-RTK for users in the 41-km reference network was 14.5 s using DSM, with 90% of the periods converging within 1 min, 95% converging within 2 min, and all periods converging within 3 min. Compared with that of DIM, the ambiguity fixation of DSM was greater. After convergence, the mean RMS values in the E, N, and U directions were 0.80, 0.93, and 2.72 cm, respectively, which represented a reduction of 11.6%, 28.8%, and 10.6%, respectively, compared with DIM. The fixing rate of ambiguity of DSM was much higher than that of DIM for large-scale networks, especially in three dimensions. The fixing rate of ambiguity in the horizontal direction with DSM was approximately 84% and that in three dimensions was 82%, which are 15.2% and 17.5% higher, respectively, than those for DIM, and the TTFF of DSM is significantly faster than that of DIM. Using DSM, the TTFF for the 98-km reference network was 33.1 s, 77% of the periods converged within 1 min, 89% converged within 2 min, and 97% converged within 3 min. After convergence, the mean RMS values in the E, N, and U directions were 1.0, 1.1, and 4.0 cm, respectively, representing a reduction of 8.2%, 29.2%, and 11.2%, respectively, compared with DIM.