Decoupling atmospheric and multipath errors in NRTK landslide monitoring : a refined multipath modelling strategy

Abstract

Network Real-Time Kinematic (NRTK) high-precision deformation monitoring is affected by the superposition of multipath effects and residual atmospheric delay. Unmodeled atmospheric errors can contaminate multipath correction models and degrade positioning accuracy if not properly separated. This study proposes a refined multipath mitigation method. First, Complementary Ensemble Empirical Mode Decomposition (CEEMD) is applied to suppress high-frequency white noise. Multichannel Singular Spectrum Analysis (MSSA) is then used to jointly decompose the denoised residuals from multiple Orbital Repetition Times (ORTs) and generate the corresponding Reconstructed Components (RCs). We then evaluate the temporal coherence of RCs across channels, components that exhibit high temporal similarity across ORTs are identified as multipath-related and are used for multipath modeling. Using data from the Hong Kong SatRef network, including various Virtual Reference Station (VRS) baseline lengths and an ultra-short baseline scenario, we demonstrate significant improvements in positioning accuracy and robustness across baselines. The proposed method consistently improved horizontal positioning by 9% and vertical positioning by 6% compared to a conventional three-day SF, and by 15–19% relative to unfiltered solutions. Moreover, heavy rainfall, as an important landslide trigger, was observed to amplify both multipath and atmospheric errors, temporarily reducing NRTK positioning accuracy.