## 3 Results
### Kinematic Automotive Data Results  
While it is important to assess the performance of the proposed algorithm with static IGS data, it is equally important to do the assessment with various test scenarios. In this case, the focus is on kinematic data using geodetic hardware. Two datasets are collected, as shown with their ground tracks in Figure 3 and processed in dual-frequency and dual-/triple-frequency modes. The triple-frequency processing is omitted as its limitations were established in the previous sections. Figure 10 shows the results for the first suburban dataset. Figure 10a,b show the improvements from both ambiguity resolution and the third frequency. The horizontal float solution (being the same in both dual-frequency and dual-/triple-frequency) converges below a decimetre after 24.5 min and has an overall rms of 12.5 cm. These statistics are greatly improved when fixing ambiguities in dual-frequency mode, as the convergence time decreases to 0.5 min, with an overall rms of 3.2 cm. The results improve further when fixing the third frequency, as instantaneous convergence is achieved with an overall rms of 1.5 cm. This level of performance is comparable to RTK and is impressive given that only ultra-rapid global orbits, clocks and biases are used in the processing. The improvements from the third frequency in this case appear in the form of correct fixing in the initial epochs when the ambiguities tend to not be estimated well enough to be fixed correctly. This impact on the correct fixing of ambiguities is shown in Figure 10d which shows the ambiguity success rate [35] for both frequency combinations: adding a third frequency leads to an increase of the success rate of the  
Figure 9: (**a**) Dual-frequency, triple-frequency, and mixed dual-/triple-frequency 3D errors for station GODS, on DOY 72, 2021 between UTC hours 5 and 6, and (**b**) number of satellites with two and three frequencies at station GODS on DOY 72, 2021.  
ambiguities, especially during the initial epochs of processing, allowing for instantaneous convergence. As was the case with the IGS data, the vertical component in this case is not estimated as well as the horizontal component. The variations in the float solution, which has an rms of 7.8 cm, are reduced when fixing the ambiguities, as the rms of the dual-frequency fixed solution is reduced to 4.7 cm, to be further reduced to 3 cm for the dual-/triple-frequency fixed solution. The higher variations that are seen in both horizontal and vertical errors after 20 min are due to the car being driven in a high-multipath area, with buildings on both sides of the road. The effect of the signal blockages on the number of satellites can be seen in Figure 10c. While the effect of the third frequency is relatively limited for this first dataset, it is more noticeable when looking at the second dataset, which was collected in an open-sky parking lot. The results from the processing are summarized in Figure 11.  
In the same fashion as for the first kinematic dataset, only one float solution is shown due to both float solutions being very similar in performance. The horizontal float solution takes a relatively long time of 35 min to converge to the decimetre-level in this case with an overall rms of 36.4 cm. Similarly to the first dataset, fixing the dual-frequency ambiguities allows for better accuracies to be reached sooner, as the solution converges within 2.7 min, with an accuracy of 32.4 cm. The relatively high rms value is due to the errors being relatively high before convergence, as the rms after convergence is 1 cm. The convergence  
Figure 10: Float and fixed dual-frequency and dual-/triple-frequency horizontal (**a**) and vertical (**b**) errors, number of satellites per frequency (**c**), and ambiguity success rate (**d**) for the first kinematic dataset collected near York University on DOY 337, 2020.  
time is further decreased to 51 s when fixing the third frequency as well, with a total rms of 5 cm, and an rms after convergence of 1 cm.