## 3 Experiment and Result Analysis
### Data Collection and Processing Strategy  
The experimental data are from six continuously operating reference stations (CORSs) at the medium latitude of China. The distribution of the six stations is shown in Figure 1. A total of 15 medium-long baselines ranging from 38 km to 167 km are formed by these six stations. All the stations are equipped with Trimble NetR9 receivers and TRM59900.00 antennas. The length and elevation differences of all the baselines are listed in Table 1. Four days of real dual-frequency GPS/BDS data were collected in 2017, the interval of the data was 30 s, and the cutoff elevation angle was set as 10\({}^{\circ}\). The GPS, BDS, and GPS/BDS-combined data from each baseline were carefully processed using the traditional and RZTD-constrained RTK models. Considering the possible wrongly fixed ambiguities in past epochs, instead of inheriting the fixed ambiguity, the ambiguity of current epochs will try to fix them again. The processing strategies of traditional and RZTD-constrained RTK are illustrated in Table 2. The empirical initial values of ambiguity, ionosphere, and RZTD parameters together with their corresponding variances in the Kalman filter were set in the same way for the two models. The process noise of the RZTD parameter was set to 0.1 mm\(/\sqrt{s}\)[10] for the two models. Different from the traditional model, the external troposphere corrections derived from the GPT2 model were introduced as pseudo-observations into every epoch for the RZTD-constrained RTK model. Together with the data from a winter day (DOY016), data from three days in the other three seasons (DOY092, 190, 289) were processed to compare the effect of the RZTD-constrained RTK model in different troposphere conditions. The ambiguity fixed rate is one indicator to assess the performance of RTK positioning. For medium-long baseline RTK, the multipath error and the residual atmospheric delays will reduce the success rate of AR. In this study, the partial ambiguity fixing (PAR) technique is proposed when a full set of ambiguities cannot be fixed. The first step is to form wide-lane (WL) and narrow-lane (NL) ambiguities from the original ambiguity (L1/L2 or B1/B2 ambiguity, derived from Section 2.2) using a transformation matrix [13]; the corresponding variance-covariance matrix for WL and NL ambiguities are also computed by the law of error propagation. The LAMBDA method [36] is applied to search for the optimal fixed value of the ambiguities; after WL ambiguities are fixed, the NL ambiguities begin to fix. In order to obtain a reliable ambiguity-fixed solution, the fixed-failure rate ratio test (FF-ratio) test [37] is applied for ambiguity validation; the fixed-failure rate is set as 1%. If the ambiguity validation fails, the ambiguity subset is selected by excluding the ambiguities whose best candidates are the same. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Baseline**} & \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Length (km)**} & \multicolumn{2}{c}{**Elevation**} & \multirow{2}{*}{**Baseline**} & \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Length (km)**} & \multicolumn{2}{c}{**Elevation**} \\  & & & **Difference (m)** & & & & Difference (m) \\ \hline DXWY-DXZX & 1 & 38.3 & 214 & DXWY-XLHZ & 9 & 85.5 & 66 \\ DXWY-DXLT & 2 & 41.2 & 160 & GNAX-GNYL & 10 & 112.9 & 311 \\ DXLT-XLHZ & 3 & 47.7 & 271 & DXZX-LZHZ & 11 & 119.8 & 280 \\ GNYL-DXLT & 4 & 48.3 & 310 & GNAX-DZXZX & 12 & 121.4 & 8 \\ GNYL-DXWY & 5 & 53.6 & 105 & GNAX-DXWY & 13 & 138.9 & 206 \\ GNYL-DXHZ & 6 & 58.4 & 39 & GNAX-DXLT & 14 & 158.0 & 1 \\ GNYL-DXZX & 7 & 74.0 & 319 & GNAX-LXLZ & 15 & 166.9 & 272 \\ DXZX-DXLT & 8 & 78.9 & 9 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Baseline information. Figure 1: Distribution of the six continuously operating reference stations (CORSs).