## 5 Results
It should be added that the parameter values \((\sigma_{x},\sigma_{y},\sigma_{z})\) are shown for two weight models, i.e., \(P_{g}=\frac{1}{m_{S_{g}}}\ \vee\ P_{g}=\frac{1}{Cl_{g}^{2}}\) and \(P_{r}=\frac{1}{m_{S_{r}}}\ \ \ \vee\ \ \ P_{r}=\frac{1}{Cl_{g}^{2}}\). When observing Figures 8-10, it can be seen that, when we use the weight in the inverse of the mean error function, we get better results than when we weight in the joint function of the number of tracked GPS or GLOMASS satellites. Therefore, the values \(\sigma_{x}\) are from 0.246 m to 0.323 m when using measuring weights \(P_{g}=\frac{1}{m_{S_{g}}}\) and \(P_{r}=\frac{1}{m_{S_{r}}}\). On the other hand, when we use measuring weights \(P_{g}=\frac{1}{Cl_{g}^{2}}\) and \(P_{r}=\frac{1}{Cl_{g}^{2}}\), then the \(\sigma_{x}\) values are from 0.071 m to 0.110 m. The \(\sigma_{x}\) values were reduced by over 66% when we used weights \((P_{g}=\frac{1}{Cl_{g}^{2}};P_{g}=\frac{1}{Cl_{g}^{2}})\) compared to weights \((P_{g}=\frac{1}{m_{S_{g}}};P_{r}=\frac{1}{m_{S_{r}}})\) in the calculations. The mean errors \(\sigma_{y}\) were from 0.571 m to 0.769 m when using weights \(P_{g}=\frac{1}{m_{S_{g}}}\) and \(P_{r}=\frac{1}{m_{S_{r}}}\). On the other hand, when we used measuring weights \(P_{g}=\frac{1}{Cl_{g}^{2}}\) and \(P_{r}=\frac{1}{Cl_{g}^{2}}\), then the \(\sigma_{y}\) values were from 0.183 m to 0.267 m. The \(\sigma_{y}\) values were reduced  
Figure 5: Residuals \((V_{g,x};V_{r,x})\) along the \(X\)-axis. Figure 6: Residuals \((V_{g,g};V_{r,y})\) along the \(Y\)-axis. by about 65% when we used weights \((P_{g}=\frac{1}{C\overline{L_{g}^{2}}};P_{g}=\frac{1}{C\overline{L_{g}^{2}}})\) compared to the application of \((P_{g}=\frac{1}{ns_{g}};P_{r}=\frac{1}{ns_{r}})\) in the calculations. The mean errors \(\sigma_{z}\) had values from 0.001 m to 0.089 m when using weights \(P_{g}=\frac{1}{ns_{g}}\) and \(P_{r}=\frac{1}{ns_{r}}\). When we used measuring weights \(P_{g}=\frac{1}{C\overline{L_{g}^{2}}}\) and \(P_{r}=\frac{1}{C\overline{L_{g}^{2}}}\), then the \(\sigma_{z}\) parameters ranged from 0.001 m to 0.029 m. In this case, it can also be said that the use of measuring weights \((P_{g}=\frac{1}{C\overline{L_{g}^{2}}};P_{g}=\frac{1}{C\overline{L_{g}^{2}}})\) reduced the mean errors \(\sigma_{z}\) by over 65% compared to the solution using \((P_{g}=\frac{1}{ns_{g}};P_{r}=\frac{1}{ns_{r}})\). The test results in Figures 11 and 12 show the \((M_{x},M_{y},M_{z})\) parameter values for the solution using measuring weights \((P_{g}=\frac{1}{ns_{g}};P_{r}=\frac{1}{n_{g}})\) and \((P_{g}=\frac{1}{C\overline{L_{g}^{2}}};P_{g}=\frac{1}{C\overline{L_{g}^{2}}})\). The \((M_{x},M_{y},M_{z})\) values for weights \((P_{g}=\frac{1}{C\overline{L_{g}^{2}}};P_{g}=\frac{1}{C\overline{L_{g}^{2}}})\) are up to 0.544 m. In turn, the \((M_{x},M_{y},M_{z})\) values for weights \((P_{g}=\frac{1}{C\overline{L_{g}^{2}}};P_{g}=\frac{1}{C\overline{L_{g}^{2}}})\) are up to 0.189 m. It can be concluded that the \((M_{x},M_{y},M_{z})\) parameters for solving a position using \((P_{g}=\frac{1}{C\overline{L_{g}^{2}}};P_{g}=\frac{1}{C\overline{L_{g}^{2}}})\) improved by about 65% to 66% compared to the corresponding results for these quantities when weights \((P_{g}=\frac{1}{ns_{g}};P_{r}=\frac{1}{ns_{r}})\) were used. Figure 8: Mean errors \(\sigma_{x}\) along the \(X\)-axis. Figure 7: Residuals \((V_{g,z};V_{r,z})\) along the \(Z\)-axis. Figure 10: Mean errors \(\sigma_{z}\) along the \(Z\)-axis. Figure 9: Mean errors \(\sigma_{y}\) along the \(Y\)-axis. Table 1 shows the results of the \(\sigma_{0}^{2}\cdot(n-1)\) chi-square test's parameters and table values \(\chi_{f,1-\alpha}^{2}\) at the \(1-\alpha=0.95\) confidence level and for \(f=n-1\) degrees of freedom. The parameter results \(\sigma_{0}^{2}\cdot(n-1)\) were smaller than the tabulated \(\chi_{f,1-\alpha}^{2}\) values, so the global chi-square statistical test, which determines the internal reliability of the performed alignment, was fulfilled and implemented. In the analyzed case, the tabular value of the chi-square test was 3.841.