## 3 Results and Discussion
### Discussion
However, using method C the share of data with no solution was increased compared to method \(B\). The increased amount of differential solutions for method \(B\) compared to method \(C\) might be the result of a better satellite signal constellation since geometrical dilution of precision (GDOP) was increased for method \(B\). The effects of satellite signal frequencies and satellite systems on the share of fixed integer ambiguities and accuracy found in this study are in line with the literature [43]. Figures 4 and 5 show that a certain amount of measurements with methods _B-D_ resulted in differences in the order of meters consisting both of integer fixed ambiguity and real number float ambiguity solutions. For the cases when large differences occurred with fixed integer ambiguity solutions might be a result of inappropriate integer ambiguity fixing and probably be a result of cycle slips. Time series analysis showed that in most of these cases the integer fixed solutions were wrong for the entire trajectory. Taking method \(D\) in condition 10\({}^{\circ}\) as an example the ambiguities of 6 trajectories were properly fixed causing small differences but the remaining six trajectories were inappropriately fixed, causing differences in the order of meters. Hence, in such cases, ambiguities were often wrongly fixed while the skier was still static prior to the start and ambiguity status did not change from fixed to float or vice versa during skiing. A reason for the inappropriate integer ambiguity fixing might be the Kalman filter adjustment to a dynamic mode that was undertaken for this study to allow tracking of the dynamic application. The dynamic mode might reduce the number of constraints and lead to difficulties in identifying cycle slips. However, the study also showed, that adding the satellite system GLONASS and frequency L2 enabled the software to identify cycle slips more easily. Figure 6 shows an example of a trajectory for which the ambiguity mode shifted from real number float ambiguity to integer fixed ambiguity mode during a run. The red circles show the positions of the first part of the turn, with real number float ambiguities and the blue circles show the section with integer fixed ambiguities. The black line represents the reference trajectory and the standalone code solution \(E\) is plotted in green. The example in Figure 6 illustrates the typical size and nature of the  
differences to the reference trajectory for the real number float ambiguity and the standalone code solution. The standalone code solution \(E\) shows increased systematic and random error compared to the differential real number float solution. Since GNSS signal obstruction can lead to loss of differential solutions, the time to fix integer ambiguities and quickly regain acceptable accuracy is crucial [38]. This study showed that time to fix integer ambiguities was shorter when both frequencies (L1 and L2) were used instead of L1 only. Hence, rapid integer ambiguity fixing is a second reason to use frequency L1 and L2 when measuring alpine ski racing under ordinary conditions. However, it is known that time to fix integer ambiguities also depends on the GNSS equipment [38] and might deviate when different GNSS devices are used. If different receivers, antenna and processing software are applied, positioning accuracy may also differ, especially if low-cost devices are used. Initial comparison of standalone low-cost devices applying L1 and GPS resulted in substantially larger differences to method \(A\) than the ones presented for method \(E\) in this study. The findings of this study might not only be applicable to the sport of alpine skiing, but also to other sports held in surroundings with variable GNSS signal obstruction. Furthermore, the results of the 10\({}^{\circ}\) condition might be applicable to all kinds of sports allowing favorable GNSS measurement conditions.