GNSS Surface Occlusion Model Analysis Based on Different Fitting Functions
Publication: Journal of Surveying Engineering
Volume 149, Issue 3
Abstract
Accuracy and reliability are determining factors when selecting where to place a global navigation satellite system (GNSS) receiver for landslide monitoring and early warning in real time. Previous studies have revealed that surface occlusion models (SOMs) are beneficial to reduce the multipath error of satellite observation when the satellite line of sight is near the ground and improve the accuracy and reliability of GNSS monitoring. This paper investigates fitting functions to determine appropriate SOMs for GNSS stations located in different spatial environments. Based on their similarity to terrain patterns, Gaussian, Fourier, and sum of sine functions were chosen to build SOMs for the rover sites with different occlusions; the performance of fitting accuracy and application on the positioning were analyzed. The results indicate that the three functions present a similar performance considering the goodness of fit, but the Fourier and sum of sine functions can significantly improve the efficacy of SOMs and enhance performance in positioning for landslide monitoring. Therefore, these two functions can effectively support accurate and reliable GNSS landslide monitoring in real time.
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Data Availability Statement
All the processed data, models, or computing programs that support the findings of this study are available from the corresponding author upon request.
Acknowledgments
This work is partly supported by the National Natural Science Foundation of China (Grant Nos. 42204043, 41974032, and 12203060), the Chinese Academy of Sciences (CAS) program of “Western Youth Scholar” (Grant Nos. Y712YR4701 and Y916YRa701), and “The Frontier Science Research Project” (Grant No. QYZDB-SSW-DQC028).
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Published In
Journal of Surveying Engineering
Volume 149 • Issue 3 • August 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jul 2, 2021
Accepted: Feb 7, 2023
Published online: Mar 28, 2023
Published in print: Aug 1, 2023
Discussion open until: Aug 28, 2023
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