GNSS high-precision quasi real-time positioning results are important reference information for the early warning of landslide disaster. The high-precision quasi real-time three-dimensional position information of the universal receivers installed on the land-slide is usually obtained by the continuous static filtering or the total estimation of the monitoring period, but the continuous static filtering cannot respond to the landslide sliding in time, and the ambiguity parameters of the total estimation cannot be fixed or fixed incorrectly when the quality of the observation data is poor. To solve the above problems, the quasi real-time landslide monitoring algorithm is proposed, which estimates the ambiguity parameters and position parameters separately in the monitoring period. Firstly, the continuous static filtering is used to solve the observation data in the monitoring period epoch by epoch, obtain the double difference float ambiguity parameters of each observation epoch, and optimize the ambiguity subset in the way of partial ambiguity resolution to improve the ambiguity fixed rate. Then, the position parameters in the monitoring period are estimated by the robust sequential least square, and the high-precision quasi real-time position parameter solution results are obtained. The test results of the observation data of Guizhou landslide monitoring net-work show that the success rate of the monitoring period solution of the algorithm scheme in this paper is 100%, which not only reflects the sliding trend of the landslide in quasi real time, but also the external coincidence accuracy of the positioning results in the plane direction and elevation direction can reach the millimeter level. The above experiments verify the feasibility and effective-ness of the proposed algorithm in quasi real-time landslide monitoring.
GNSS High Precision Quasi Real-Time Landslide Monitoring Algorithm for the Universal Receivers
Lect. Notes Electrical Eng.
China Satellite Navigation Conference ; 2022 ; Beijing, China May 22, 2022 - May 25, 2022
2022-05-07
14 pages
Article/Chapter (Book)
Electronic Resource
English
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