A Refined Multipath Correction Model for High-Precision GNSS Deformation Monitoring - Yan Chen; Ran Lu; Xingyu Zhou; Mingkun Su; Mingyuan Zhang (Remote Sensing)
In deformation monitoring, the severe GNSS multipath caused by reflective surfaces can significantly degrade positioning accuracy. However, traditional multipath mitigation methods often assume strong day-to-day repeatability of residual errors, which is not always valid in complex monitoring environments. We propose a novel GNSS multipath correction approach that leverages multi-day post-fit residual data and principal component analysis to extract stable multipath signals, integrating them into an enhanced spatial repeatability multipath correction model. This method can effectively isolate true multipath errors, even under conditions of weak inter-day repeatability. Experimental results from a dam monitoring network demonstrate that the proposed method reduces the root mean square (RMS) error of single-day kinematic positioning by about 1.8 mm, 2.4 mm, and 6.7 mm in the East, North, and Up components, respectively. For static positioning solutions over 1 h, 2 h, and 4 h sessions, the RMS in East, North, and Up is reduced by approximately 40% on average. After correction, 2 h sessions achieve ~1.1 mm horizontal and ~3.0 mm vertical accuracy, while 4 h sessions reach ~0.9 mm horizontal and ~2.5 mm vertical accuracy. These improvements confirm that the proposed method effectively mitigates multipath effects and meets the high-precision requirements of deformation monitoring.