## 1 Introduction
Due to the redundancy of GNSS observations, an inaccurate stochastic modeling may result in high-precision observations being rejected. It is important that the observations with higher precision are characterized by higher weights (i.e., smaller variances), and, consequently, have a substantial impact on the estimated parameters. In most cases, the weighting of observations depends on the elevation angle of satellites. A low satellite is burdened with greater errors caused by the atmosphere and multipath and is characterized by higher noise and lower signal strength. In order to disregard such low angle satellites, a dedicated elevation mask is introduced. The problem of excluding or reweighting observations at low elevation angles was raised in 1997 at the IGS Workshop on Governing Board Meeting in Pasadena [28]. It has been shown that different Analysis Centers (ACs) use different approaches, e.g., some ACs include observations above an elevation angle of \(15^{\circ}\) or \(20^{\circ}\) without weighting functions, and some use \(15^{\circ}\) with an elevation angle dependent on weighting. A further issue was that most stations had high cut-off elevation angles set on the receivers, making it impossible to observe all satellites. From the IGS [29], one can read that the use of low satellites improves height determination, and it has been recommended to use observations down to \(5^{\circ}\). Furthermore, in 1997, the AC Center for Orbit Determination in Europe (CODE) made changes to its calculations [30]. The cut-off elevation angle was changed from \(20^{\circ}\) to \(10^{\circ}\) and an elevation-dependent weighting of the observations was applied: \(\frac{1}{\cos^{2}\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! being fully operational and the poor quality of products. Both the accuracy improvement and the convergence time were reduced. [PERSON] et al. [33] showed the effect of various observations' weighting for multi-GNSS PPP and noted that different weights for each GNSS system assumption result in higher position accuracy. The best weighting was obtained when taking into account the Signal-In-Space Range Error (SISRE) parameter, which shows the quality of products for a given system. [PERSON] et al. [34] proposed weight factors when searching algorithms with a moving-window average filter to ionospheric delay constraint for GPS and Galileo real-time positioning, in order to obtain a shorten convergence time. [PERSON] et al. [35] performed analysis with different IGS products and used two weighting methods, i.e., constant precision of observations and exponential weighting functions for GPS, to prove the improvement in accuracy and shortened convergence time while using the exponential function. [PERSON] et al. [36] studied the four different stochastic modelling of inter-system bias (ISB) using piece-wise constant, random walk, arc-dependent constant and white noise processing strategies for GPS and BeiDou. The analysis showed that the results depend on the used IGS products from different ACs. [PERSON] et al. [37] studied the impact of different weight ratio between pseudorange and phase observations again for GPS and BeiDou. [PERSON] et al. [38] performed the analysis for multi GNSS real-time PPP for uncombined and ionosphere-free combined observations, using different weights for different constellations and blocks of satellites. This analysis used SISRE parameters and sinus functions with elevation angles. Again, it improved accuracy and shortened convergence time. [PERSON] et al. [11] analyzed two methods of stochastic modelling for triple-frequency PPP ambiguities resolutions (PPP-AR) for multi-GNSS. Analyses was performed using different a priori precision of observations from zero baseline experiment. Also in this work, the sine function for satellites with an elevation angle below 30\({}^{\circ}\) and the equal precision for satellites with an elevation angle above 30\({}^{\circ}\) were employed.