## 4 Experimental Results and Discussion
### Experiments on Simulation Datasets
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**Conditional Control Group A**} & \multicolumn{3}{c}{**Experimental Group B**} \\ \cline{2-7}  & **Max Error** & **RMSE** & **Fix Rate** & **Max Error** & **RMSE** & **Fix Rate** \\ \hline Single Epoch RTK & 2.62 & 0.85 & 99.83\% & 321.77 & 20.66 & 92.78\% \\ TC-RTK/INS (1) & 2.59 & 0.85 & 100\% & 4.27 & 1.09 & 99.72\% \\ TC-RTK/INS (2) & 2.61 & 0.85 & 100\% & 17.77 & 1.33 & 98.06\% \\ ARPD-RTK/INS & 2.57 & 0.85 & - & 4.59 & 1.06 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Trajectory error and fix rate for different RTK algorithms with small pseudorange errors (Unit: cm). RMSE: root mean square error. By analyzing the error results in Table 2, we can conclude as follows. * By comparing the positioning results of the four methods under Scene A, we can find that a similar performance can be obtained by four different methods when under good satellite observation conditions. The maximum position error is about 3 cm, which meets the desired high-precision requirement. * The number of available satellites plays an important role in the accuracy of the positioning algorithm. Comparing the single epoch RTK results in Scene A and Scene B, it is found that the fix rate decreases as the number of satellites decreases. Even if additional inertial information is considered in the AR process, such as the TC-RTK/INS (2) algorithm, the final fix rate is also affected by the number of visible satellites. For the other two algorithms that do not involve the fix rate change, the overall accuracy also slightly degraded due to a reduction in the number of satellites. Figure 8: (**a**) Error distribution in Earth-Centered Earth-Fixed (ECEF)-XY axis under Scene A; (**b**) Error distribution in ECEF-XY axis under Scene B.  
Figure 7: (**a**) The satellite visibility of the 1800-s simulation data for the control group; (**b**) The satellite visibility for experimental group; (**c**) The number of satellites in Scene A and C; (**d**) The number of satellite in Scene B and D.  
* Comparing the performance of the four algorithms in Scene B, methods 1 and 3 both show that the fix solution cannot be obtained all of the time. It can be seen that the AR process with INS-aiding has a higher fix rate than the single-epoch RTK. When the AR process failed, the positioning accuracy will be degraded, which depends on the quality of the pseudorange observation. Since we assume that all of the cycle slips can be detected before the positioning algorithm is performed, even in the scene where the satellite observability frequently changes, the TC-RTK/INS (1) algorithm will not be influenced by the wrong ambiguity and remain the ideal positioning accuracy. The reason why the ARPD-RTK/INS and TC-RTK/INS (1) algorithm perform better than the other two algorithms is that the position predicted by the INS measurement is more accurate than the position derived from the pseudorange estimation, so that a more effective ambiguity search region can be obtained. If we want to embody the advantages of the ARPD-RTK/INS over the TC-RTK/INS (1), one way is to add cycle slips that cannot be detected before performing the positioning algorithm. As a result, the TC-RTK/INS (1) algorithm, which contains float ambiguities in the filter states, will deviate from the true value during the filtering process. Another way is to change the quality of pseudorange observations. Thus, more observation noise is added to the pseudorange measurements while keeping the satellite visibility of Scene A and Scene B unchanged.