Observation-Level PCA and Machine Learning for Multipath Mitigation in GNSS PPP with Tropospheric Wet Delay Assessment

Precise Point Positioning (PPP) provides high accuracy positioning using a single GNSS receiver, but its performance is often limited by station-specific multipath errors. This study presents a novel Principal Component Analysis (PCA) and machine learning based Multipath Hemispherical Map (PM-MHM) approach to mitigate multipath effects in PPP mode. Network-wide correlated errors (NWCEs), including satellite orbit and clock errors as well as unmodeled tropospheric wet delays, are first isolated and removed using PCA, allowing the remaining station-specific residuals to be interpreted as multipath. The PM-MHM employs a hybrid machine learning framework that integrates a global model with localized, grid-specific correction models to adaptively capture multipath patterns. In this study, we develop an automatic fitting training scheme that evaluates multiple algorithms, including Random Forest, Least Squares Boosting and Extreme Gradient Boosting. The model is trained on six consecutive days of PPP residuals and evaluated on independent datasets, demonstrating superior performance compared with the Trend Surface Analysis-based MHM (T-MHM). For pseudorange and carrier phase observations, PM-MHM achieves mean RMSE reductions of 39.8% and 37.3%, respectively, outperforming T-MHM by 10-15%. Furthermore, PCA decomposition of Up-component residuals reveals that the low frequency portion of the first principal component (PC1_low) effectively captures tropospheric zenith wet delay (ZWD) variations. Incorporation of PC1_low into GNSS-derived ZWD improves correlation with radiometer measurements by about 0.08 and reduces RMSE by 6.32%. These results demonstrate that PM-MHM not only offers high accuracy multipath mitigation but also enables the physical analysis of other residual components beyond multipath, highlighting its potential for improved PPP-based atmospheric monitoring and high precision positioning applications.