At present, high-precision GNSS positioning technology is an important means to monitor the health of bridges and other structures. However, the GNSS signal of reference stations and monitoring stations used for bridge monitoring can easily be blocked by bridge towers, vehicles, or other objects, resulting in low positioning accuracy and insufficient availability of GNSS, which affects the effectiveness of bridge structural health monitoring. Therefore, according to the characteristics of bridge structure, this paper proposes to take the bridge tower monitoring station as a moving-base station to build the baselines with other monitoring stations and use the moving-base RTK-GNSS method to realize the relative positioning, so as to improve the availability of GNSS in the application of bridge structure health monitoring. In this paper, the moving-base RTK-GNSS model is derived and verified via GNSS monitoring data of the Ganzhou dedicated high-speed railway bridge. The results show that the ambiguity in the fixing rate can be improved using the moving-base RTK-GNSS method with the tower monitoring station as the reference station. The deformation and vibration characteristics of each monitoring point can be reflected, and the displacement and vibration amplitude estimation accuracy can achieve results better than 4 mm. Therefore, the moving-base RTK-GNSS method can be used as an alternative scheme when the observation environment of the base station is poor or the banded engineering monitoring is applied, so as to improve the monitoring capability of GNSS.

Global Navigation Satellite System (GNSS) can provide high-precision three-dimensional real-time or quasi-real-time changes of monitoring points automatically in house monitoring applications. However, due to the signal sheltering problem, large observation noise and multipath effects in urban observing environment with dense buildings, ambiguity resolution would be hard, and GNSS accuracy cannot always achieve millimeter level to satisfy the requirement of house monitoring. Static level is a precision instrument for measuring elevation difference and its variations, with a precision up to sub-millimeter level. It could be integrated with GNSS to improve the positioning accuracy in height direction. However, the existing integration of GNSS and static level is mostly on a respective results level. In this study, we proposed a method of integrating GNSS and static level observations tightly to enhance the GNSS positioning performance. The hardware design and integration mathematic model in data processing were introduced, and a group of experiments were carried out to verify the performance in positioning with and without the static level observation constraints. It found that the vertical monitoring measurement results of static level can achieve less than 1 mm. The GNSS ambiguity resolution performance can be improved by incorporating the measurement of static level into GNSS positioning equation as external constraints, and the precision of GNSS float solutions was significantly improved. Finally, the static level constraint can further improve the accuracy of the fixed solution from about 2 cm to better than 2 mm in vertical direction, which is even better than the accuracy in horizontal directions with about 3–6 mm with the static level constraint. The tight combination data processing algorithm can significantly improve the working efficiency, accuracy, and reliability of the application of dangerous house monitoring.