Abstract:
Objectives In the complex landslide monitoring environment, the global navigation satellite system (GNSS) signals are affected by factors such as atmospheric delay, multipath effect, and local occlusion, resulting in the elevation or signal-to-noise ratio stochastic model based on a single index can not meet the high-precision positioning in complex scenes. Meanwhile, variations in data quality among stations using multi-mode and multi-frequency GNSS observations further increase the demand for accurate and reliable stochastic modeling.
Methods In order to solve this problem, a GNSS comprehensive stochastic model based on empirical cumulative distribution normalization optimization is proposed. The model is constructed based on elevation and signal-to-noise ratio information, and the weight coefficient is used to refine the influence of differences between single model, multi-frequency and multi-system and stations on the stochastic model.
Results The experimental results under high occlusion environment show that the average fixed rate of the new model for ten consecutive days is 97.8%, which is 24.4%, 25.6% and 15.3% higher than that of the elevation stochastic model, the signal-to-noise ratio stochastic model and the comprehensive stochastic model with principal components analysis, respectively. The root mean square error of the fixed solutions is 0.007 m in the east/west and north directions and 0.012 m in the up direction.
Conclusions The new model improves the epoch fixed rate while the accuracy of fixed solution can meet the requirement of GNSS centimeter positioning in complex landslide monitoring environment.
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