###### Abstract
Essentially, SF uses constellation repetition to identify the closest residuals from post-processing and applies them to mitigate the multipath error in the current observation. The second method is the grid-based technique, which mitigates multipath effects by stacking residuals within azimuth and elevation grids, usually \(1^{\circ}\times 1^{\circ}\)[16; 17; 18; 19; 20]. Both methods aim to model and calibrate multipath errors by leveraging spatial autocorrelation. However, these methods handle spatial autocorrelation data only approximately, limiting their effectiveness. Actually, spatial autocorrelation can be more accurately modeled using covariance functions, applied through techniques such as Least-Squares Collocation (LSC). These functions can be homogeneous and isotropic, depending only on distance and exhibiting rotational symmetry, or anisotropic, where spatial orientation affects their values [21; 22; 23; 24]. This approach significantly improves the precision of spatial autocorrelation modeling compared to traditional sidereal and grid methods. Moreover, the growing number of satellites and the increasing frequency overlap among GNSS systems have made signal compatibility and interoperability key trends in high-precision data processing for multi-GNSS applications. These advancements offer new perspectives on improving multipath correction. Research by [PERSON] et al. [25] highlights a strong correlation in the residuals of overlapping frequencies across GPS, BDS-3, and Galileo at the same spatial location. Similarly, [PERSON] et al. [26] demonstrate that the interoperability of GPS, Galileo, and BDS-3 enhances spatial resolution, improves the efficiency of multipath modeling, and boosts correction performance. These findings suggest that using overlapping frequencies can significantly improve multipath mitigation. However, there remains considerable interest in exploring the common multipath characteristics across various GNSS frequencies, including non-overlapping ones. Understanding these characteristics could further enhance the effectiveness of spatial autocorrelation-based multipath mitigation strategies. Additionally, due to various perturbation factors, all satellite orbits experience shifts [27; 28; 29], which result in the residual values of satellites after an orbit drift no longer accurately representing the multipath errors of their pre-drift positions. Consequently, multipath modeling must be reconstructed, and new residuals from post-processing are required to account for these shifts. In contrast, if the constellations remain stable and the surrounding environment of the station remains unchanged, a single model can be used consistently over time without the need for updates. However, it remains unclear whether all satellite constellations drift far enough to significantly impact multipath mitigation. Therefore, investigating the extent of constellation drifts within GNSS systems and their potential effects on multipath mitigation is of particular interest. To overcome the limitations of traditional multipath mitigation methods and better leverage the spatial autocorrelation of GNSS data, this study proposes a simplified autocorrelation-based multipath mitigation method utilizing unified covariance parameters. Specifically, this method investigates the spatial cross-correlation of residuals across different GPS and Galileo frequencies, analyzing the relationship between the covariance function parameters of the residuals and evaluating the impact of constellation shifts on long-term multipath mitigation. Based on this analysis, the method applies multi-GNSS data for multipath mitigation, simplifying the modeling process while maintaining the performance, thereby improving the short- and long-term positioning accuracy. This study introduces the multipath modeling techniques and the mathematical tools used for analysis in Section 2.