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Real-time kinematic (RTK) positioning technology is widely used in landslide monitoring because of its unique advantages of real-time and high precision. However, in the complex landslide environment with severe occlusion, multipath error has become one of the main error sources affecting GNSS high-precision positioning. The sidereal filtering method is suitable for monitoring scenarios containing multipath errors and white noise. For special scenarios with large deformation in the imminent slipping stage and monitoring data interruption, the multipath error model constructed by the traditional method is not accurate enough to retain its deformation trend. In this paper, an improved sidereal filtering based on sliding segmentation correlation is used to identify the new stage of landslide considering the mutation point, establish the available dataset, and subsequently match the multi-day correlation data using sliding segmentation and perform mean processing to construct a multipath model. It is applied to two typical stages of the Heifangtai landslide, stable and imminent, and compares the effectiveness of traditional sidereal filtering, conventional segmentation method and the method in this paper by qualitative and quantitative methods. The experimental results show that the method proposed in this paper performs best in coordinate deviation, fitting deviation and smoothness, avoids the problem of inter-segment jump, effectively mitigates the multipath error, and retains the deformation characteristics of the landslide. It provides more reliable data for landslide early warning systems, timely releases early warning information, and avoids the possible casualties and property losses caused by this loess landslide., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

For real-time precise point positioning (PPP) users, the State Space Representation (SSR) messages from the server are usually transmitted by wireless data link. Due to unstable communication links in practical applications, it is common that the real-time orbit and clock streams are delivered to the users at certain time delays. For multi-GNSS combined positioning, the satellite orbit and clock types for GPS, GLONASS, BDS, and Galileo are different, the ability to maintain orbit and clock accuracy will be of different characteristics when SSR delay occurs. In this condition, it is difficult to maintain the real-time precision positioning accuracy and availability with conventional models. In this study, the orbit and clock extrapolating errors are evaluated based on GPS, GLONASS, BDS-2, BDS-3, and Galileo SSR products from CNES; and the performance of real-time positioning during outages of SSR correction are also analyzed for these four systems. The experiment results show that the accuracy of the orbital clock extrapolation decreases with the increase of the SSR delay time. However, significant differences in the orbital clock extrapolation errors are observed for different GNSS satellites under the same SSR delay. For 10 minutes SSR delay, the GLONASS signal-in-space range errors (SISREs) caused by orbit and clock offset extrapolation can reach 20.9 and 7.3 cm, respectively, while the BDS and Galileo SISREs caused by orbit and clock offset extrapolation can be kept within 2.2 cm, the clock offset has been converted to distance by the light speed. The performance of BDS and Galileo real-time positioning under SSR delays are significantly better than that of GLONASS and GPS. Compared with the traditional model, the four-system combined real-time positioning performance under SSR delay situation is significantly improved when ultra-rapid precise orbits are used and the clock extrapolation error characteristics for different satellites are considered. Using this new model, the positioning accuracy in the horizontal and vertical directions can maintain at 1.7 and 2.1 cm under 10 minutes SSR delay. This research has important reference value for improving the robustness of real-time positioning and optimizing the SSR update rate.

Global navigation satellite system technology has been widely used for high-precision, real-time monitoring of landslides. To improve forecasts and early warnings, the true deformation features must be extracted from the global navigation satellite system monitoring series. However, as the deformation rate changes at different creep stages, the relationship between noise and true deformation may also change, making it difficult to accurately describe the deformation. In this study, an adaptive sliding window algorithm is proposed to account for this relationship change. First, the window was defined with an equal window width and step length, which improved the efficiency of feature extraction. Second, the median and normalized interquartile ranges were used to estimate the window samples and obtain a continuous and reliable series. Finally, the window sample breakdown point was defined to adjust the window parameter. These steps were repeated for the adjusted window to achieve adaptive processing of the monitoring series. The results based on both simulated and real landslide monitoring series demonstrated that the proposed method can provide adaptive, robust, and reliable deformation information for landslide warnings. The adaptive sliding window method also successfully assisted in the early warning of a loess landslide in Heifangtai, Gansu province, northwest of the Chinese Loess Plateau, indicating its practical application potential.

Aiming at the lack of high accurate antenna phase correction (PCC) model for the BDS-2 IGSO and MEO satellites, in the BDS-2 and BDS-3 satellites joint precision orbit determination (POD) and precise point positioning (PPP), in this paper, an improved method for estimating PCV and z-offset parameters is used to refine the B1I/B3I ionosphere-free linear combination PCC model for the BDS-2 IGSO and MEO satellites. Compared with the results obtained using the ground-calibrated PCO model published by BeiDou official, the STD of SLR residuals obtained by the POD using the refined PCC model is reduced by 0.6~2.4 cm, and the improvement percentage is about 8.6%~33.3%. The floating PPP solution results, based on the refined BDS-2 satellite PCC model in this paper and the existing BDS-3 PCC model, have been significantly improved by 9.5 mm (37.2%) in the elevation direction.

Three-dimensional water vapor distribution information with high spatial and temporal sampling can be obtained all-weather by using GNSS water vapor tomography technology, which has become a major research hotspot in GNSS meteorology. At present, the traditional tomography method based on single navigation system has some shortcomings, such as low utilization rate of observation data and uneven distribution of observation. This paper designs and implements a multi-GNSS water vapor tomography method which takes into account the boundary incident signal. The accuracy of the method is verified by using the observation data of Hong Kong CORS network and radio sounding products. The improvement of the results of water vapor tomography by introducing boundary incident signal and multi-GNSS signal is analyzed in detail. The results show that compared with the traditional GPS tomography method, the proposed water vapor tomography method, which combines the advantages of boundary incident signal and multi-GNSS signal, can obtain more accurate and reliable three-dimensional water vapor information.

Implementing the high-precision, real-time and three-dimensional deformation monitoring for the landslide area, which is the prerequisite for the accurate warning of landslide disasters. GNSS technology is currently the only way to directly obtain the three-dimensional vector deformation of landslide disaster surface, but GNSS has two problems of high-cost and low-reliability in large-scale landslide monitoring. The ideas of Internet of Things and the concept of "cloud platform plus monitoring terminal" are proposed in this article. Thus, we develop a real-time BeiDou/GNSS monitoring equipment with thousands RMB cost. The millimeter monitoring and warning cloud platform also are developed independently. This equipment successfully applied to real-time monitoring and early warning of Heifangtai landslide in Gansu Province. Cooperated with early warning system of the Chengdu University of Technology, we issued an accurate warning signal 40 minutes in advance to avoid casualties and property losses. The remote video surveillance installed on the landslide body recorded the whole process of landslide disaster for the first time.