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The carrier phase multipath effect is one of the most significant error sources in the precise positioning of BeiDou Navigation Satellite System (BDS). We analyzed the characteristics of BDS multipath, and found the multipath errors of geostationary earth orbit (GEO) satellite signals are systematic, whereas those of inclined geosynchronous orbit (IGSO) or medium earth orbit (MEO) satellites are both systematic and random. The modified multipath mitigation methods, including sidereal filtering algorithm and multipath hemispherical map (MHM) model, were used to improve BDS dynamic deformation monitoring. The results indicate that the sidereal filtering methods can reduce the root mean square (RMS) of positioning errors in the east, north and vertical coordinate directions by 15%, 37%, 25% and 18%, 51%, 27% in the coordinate and observation domains, respectively. By contrast, the MHM method can reduce the RMS by 22%, 52% and 27% on average. In addition, the BDS multipath errors in static baseline solutions are a few centimeters in multipath-rich environments, which is different from that of Global Positioning System (GPS) multipath. Therefore, we add a parameter representing the GEO multipath error in observation equation to the adjustment model to improve the precision of BDS static baseline solutions. And the results show that the modified model can achieve an average precision improvement of 82%, 54% and 68% in the east, north and up coordinate directions, respectively.

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The spatio-temporal random effect (STRE) model, a type of spatio-temporal Kalman filter model, can be used for the fusion of the Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) data to generate high spatio-temporal resolution deformation series, assuming that the land deformation is spatially homogeneous in the monitoring area. However, when there are multiple deformation sources in the monitoring area, complex spatial heterogeneity will appear. To improve the fusion accuracy, we propose an enhanced STRE fusion method (eSTRE) by taking spatial heterogeneity into consideration. This new method integrates the spatial heterogeneity constraints in the STRE model by constructing extra-constrained spatial bases for the heterogeneous area. The effectiveness of this method is verified by using simulated data and real land surface deformation data. The results show that eSTRE can reduce the root mean square (RMS) of InSAR interpolation results by 14% and 23% on average for a simulation experiment and Los Angeles experiment, respectively, indicating that the new proposed method (eSTRE) is substantially better than the previous STRE fusion model.