Asynchronous RTK Method for Detecting the Stability of the Reference Station in GNSS Deformation Monitoring

Abstract

The real-time kinematic (RTK) positioning technique of global navigation satellite systems (GNSS) has been widely used for deformation monitoring in the past several decades. The RTK technique can provide relative displacements in a local reference frame defined by a highly stable reference station. However, the traditional RTK solution does not account for reference stations that experience displacement. This presents a challenge for establishing a near real-time GNSS monitoring system, as since the displacement of a reference station can be easily misinterpreted as a sign of rapid movement at the monitoring station. In this study, based on the reference observations in different time domains, asynchronous and synchronous RTK are proposed and applied together to address this issue, providing more reliable displacement information. Using the asynchronously generated time difference of a reference frame, the proposed approach can detect whether a measured displacement has occurred in the reference or the monitoring station in the current epoch. This allows for the separation of reference station movements from monitoring station movements. The results based on both simulated and landslide monitoring data demonstrate that the proposed method can provide reliable displacement determinations, which are critical in deformation monitoring applications, such as the early warning of landslides.

An Early Warning Intelligent Algorithm System for Forest Resource Management and Monitoring

“…By comparing HH, HV, difference value (HH-HV), and ratio value (HH/HV) of common land cover types, the threshold value of extracting forest information based on PALSAR/PALSAR-2 data in this study is determined [ 13 , 14 ]. In order to make the classification threshold more reliable, we exclude 2.5% pixels with maximum and minimum backscattering coefficients in HH, HV, HH-HV, and HH/HV and determine the decision tree classification threshold for extracting forest information according to the 95% confidence interval.…”

Deformation Feature Extraction for GNSS Landslide Monitoring Series Based on Robust Adaptive Sliding-Window Algorithm

“…For a three-stage creep curve, time-to-failure predictions can be made using existing methods (Xu et al, 2011;Intrieri et al, 2019). The global navigation satellite system (GNSS), which captures global, continuous, and high-precision geospatial data, has been widely used for monitoring landslides and obtaining creep curves (Benoit et al, 2014;Du et al, 2020). However, GNSS positioning technology is often affected by the external environment, multipath effect, atmospheric delay, receiver noise, and other hardware influences, causing the monitoring series to create noise (Amiri-Simkooei, 2008;Li et al, 2016;Han et al, 2018).…”

Improving the Orbits of the BDS-2 IGSO and MEO Satellites with Compensating Thermal Radiation Pressure Parameters

“…The systematic orbit errors were also identified in the International GNSS Service (IGS, [16])-combined Multi-GNSS orbit solutions [17]. The radial orbit accuracy relying on the dynamic model is essential for GNSS solutions and scientific applications [18][19][20]. In the absence of a physics-based SRP for the BDS-2 IGSO and MEOs, the studies have typically used an empirical SRP model.…”