\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**GNSS System**} & \multicolumn{2}{c}{**Between**} & \multirow{2}{*}{\(\sigma_{\psi_{ij}}\) (mm2)} & \multirow{2}{*}{\(\sigma_{\psi_{ij}}\) (mm2)} & \multirow{2}{*}{\(\hat{\rho}_{i,j}\)} & \multirow{2}{*}{\(\sigma_{\hat{\rho}_{i,j}}\)} \\  & **Dovers** & & & & \\ \hline  & C1-P2 & 6006.4 & 9.27 & 0.19 & 0.019 \\  & C1-L1 & 18.88 & 0.014 & 0.035 & 1.64\(\times 10^{-3}\) \\ GPS & C1-L2 & 18.30 & 0.012 & 0.029 & 1.64\(\times 10^{-3}\) \\  & P2-L1 & 17.27 & 0.017 & 0.018 & 1.63\(\times 10^{-3}\) \\  & P2-L2 & 22.75 & 0.014 & 0.046 & 1.63\(\times 10^{-3}\) \\  & L1-L2 & 18.36 & 1.77\(\times 10^{-4}\) & 0.610 & 0.032 \\  & C1-P2 & 4905.7 & 9.314 & 0.25 & 0.029 \\  & C1-L1 & 11.39 & 0.006 & 0.024 & 2.53\(\times 10^{-3}\) \\ GLONASS & C1-L2 & 8.05 & 0.005 & 0.008 & 2.5\(\times 10^{-4}\) \\  & P2-L1 & 10.72 & 0.008 & 0.011 & 2.51\(\times 10^{-4}\) \\  & P2-L2 & 17.39 & 0.008 & 0.064 & 2.52\(\times 10^{-4}\) \\  & L1-L2 & 11.20 & 5.04\(\times 10^{-5}\) & 0.52 & 0.045 \\  & C1-P2 & 3661.80 & 9.55 & 0.13 & 0.039 \\  & C1-L1 & 3.75 & 0.013 & 0.007 & 3.08\(\times 10^{-4}\) \\ Galileo & C1-L2 & 3.94 & 0.007 & \(-0.004\) & 3.12\(\times 10^{-4}\) \\  & P2-L1 & 9.51 & 0.013 & 0.012 & 3.05\(\times 10^{-3}\) \\  & P2-L2 & 9.82 & 0.006 & 0.036 & 3.07\(\times 10^{-3}\) \\  & L1-L2 & 7.88 & 3.87\(\times 10^{-5}\) & 0.448 & 0.054 \\  & C1-P2 & 386.59 & 24.33 & \(-0.057\) & 1.11\(\times 10^{-3}\) \\  & C1-L1 & 3.98 & 0.003 & 0.023 & 7.76\(\times 10^{-3}\) \\ BeiDou & C1-L2 & 1.89 & 0.001 & 0.019 & 7.92\(\times 10^{-3}\) \\  & P2-L1 & 2.69 & 0.006 & 0.003 & 7.59\(\times 10^{-4}\) \\  & P2-L2 & 1.52 & 0.003 & \(-0.002\) & 7.6\(\times 10^{-4}\) \\  & L1-L2 & 1.11 & 2.45\(\times 10^{-6}\) & 0.197 & 0.103 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Covariances and correlation coefficient and their precision between the observation types for each system for LEICA GR30 receiver over a very short baseline. Figure 8: Estimated correlation coefficients between the C1 and P2 observations (dashed line) with their average values in 10 epoch group windows (bold line) over a very short baseline. striking and for GPS is the highest with Trimble NETR9 and LEICA GR30. At the same time, BeiDou has the lowest correlation values in these two receivers among other systems. One of the most critical steps in high-precision GNSS positioning, as previously indicated, is the IAR, which is influenced by a realistic estimate of the covariance matrix. The LAMBDA approach was employed in this paper as a integer least-squares ambiguity estimation method, resulting in the highest probability of a valid integer estimation. However, due to the risk of applying an incorrect integer solution, which could result in a large positioning error, the resolution of ambiguity includes not only integer estimation, but also validation. The integer bootstrapping ambiguity success rate (SR) is used as a critical metric to determine the reliability of integer ambiguity resolution. It displays the fraction or percentage of IAR that were successful. The bootstrapping method begins with the most precise float ambiguity becoming rounded. According to [54], to enhance the bootstrapped success rate, one should work with decorrelated ambiguities rather than the original DD ambiguities, which have a significant correlation. Therefore, \(Z\)-transformation is used to transform the original ambiguities and the covariance matrix in the following form that \(\hat{z}=Z^{T}a\) and \(Q_{zz}=Z^{T}Q_{\hat{z}\hat{z}}Z\). So, the SR of integer bootstrapping can be estimated as follows [55]:  
\[P_{s}=\prod_{i=1}^{n}(2\Phi(\frac{1}{2\sigma_{z_{i|}}})) \tag{23}\]  
in which n represents the available ambiguities and the \(i^{th}\) ambiguity derived through a conditioning on the previous \(I\)=\(\{i+1,\ldots,n\}\) consecutively rounded ambiguities, is denoted by the short-hand notation \(\hat{z}_{i|I}\).