The Global Navigation Satellite System (GNSS) is currently one of the important tools for landslide monitoring and early warning.However, the majority of GNSS devices are installed in mountainous areas and a variety of vegetation.These harsh environments lead to defective signals at high elevation angles, rendering real-time successive and reliable positioning results for monitoring difficult.In this study, an environmental model derived from signal-to-noise ratio ARROWHEAD (SNR) is proposed to enhance the precision and convergence time of positioning in harsh environments.
A series of experiments are conducted on weighting and ambiguity-fixed models to evaluate performance.The results indicate that the proposed SNR-dependent environment model could lead to a significant improvement Insulated Food/Beverage copyright Parts in precision and convergence time; with an obtained root mean squared result on the millimeter level, a convergence time of a few seconds, and utilization which could reach 100%, for continuous and reliable positioning results.These results indicate that the proposed SNR-dependent environment model enhances the performance of GNSS monitoring and early warning to provide continuous and reliable positioning results in real-time.