Displacement detection based on Bayesian inference from GNSS kinematic positioning for deformation monitoring

Displacement is an important parameter in engineering analysis in structural mechanics and geomechanics. For decades, displacement detection based on the Global navigation satellite system (GNSS) has increasingly been important for a wide range of applications, from landslide monitoring, subsidence survey, to industrial measurement. However, due to the influence of measurement noise, it is still a challenge to identify and extract displacement from GNSS kinematic positioning results. To resolve this, we propose a novel displacement detection approach with the purpose of identifying and extracting displacement from GNSS kinematic positioning. Specifically, we use the Bayesian inference to obtain the displacement change time from the coordinate time series of GNSS kinematic positioning. By investigating the posterior distribution of the designed change point parameter, we can identify the change points. Furthermore, we derive the mean value from the posterior distribution of the mean parameter, and further obtain the displacement. Results from simulation and field experiments have demonstrated the effectiveness and flexibility of the proposed method. For significant displacement, it can be clearly identified; for small displacement, it can be identified by adding an interval constraint prior. The accuracy of vertical displacement extraction from GNSS real-time kinematic positioning can reach within 2 mm in 15 min.

Automated summary

A novel displacement detection approach is proposed using Bayesian inference to identify and extract displacement from GNSS kinematic positioning. It can accurately extract vertical displacement, clearly identifying significant displacement and adding an interval constraint for small displacement. The accuracy of vertical displacement extraction from GNSS real-time kinematic positioning can reach within 2 mm in 15 min.