Improving the accuracy of RTK positioning with the multipath hemispherical map model: A case study in GNSS landslide monitoring

Landslides are destructive geological disasters that can cause heavy casualties and huge economic losses. Global navigation satellite system (GNSS) can provide real-time, continuous, and high-precision three-dimensional deformation information all weather and all time, and thus has been widely used in landslide deformation monitoring. However, the strong multipath effect that exists under the practical observational environment will seriously affect the accuracy and reliability of GNSS real-time landslide monitoring. In this contribution, we aim to evaluate the performance of the multipath hemispherical map (MHM) model for multipath correction in landslide monitoring with GNSS real-time kinematic (RTK) positioning. Both the performance of the single-differenced MHM (SD-MHM) and double-differenced MHM (DD-MHM) models are preliminarily evaluated and compared with two GNSS datasets collected in practical landslide monitoring scenarios. The experimental results verify the spatial repeatability of the multipath error with the DD-MHM and SD-MHM models. It is also demonstrated that both the SD-MHM and DD-MHM models can effectively reduce the impact of low-frequency multipath error and improve RTK positioning accuracy. Moreover, the SD-MHM model outperforms the DD-MHM model. Compared with RTK positioning with no multipath correction (NON-MHM), the root-mean-square (RMS) errors of the phase and code residuals for the SD-MHM model are reduced by approximately 49.4% and 24.4%, respectively, and are reduced by approximately 39.8% and 18.6% for the DD-MHM model, respectively. Moreover, compared with the NON-MHM model, the positioning accuracy of the DD-MHM model is improved by approximately 25.9% and 46.5% in the horizontal and vertical components, respectively, and is further improved by approximately 37.0% and 54.2% with the SD-MHM model, respectively. The results verify the effectiveness of the MHM model in improving the RTK performance in practical GNSS landslide monitoring applications.