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GNSS -- global navigation satellite systems : GPS, GLONASS, Galileo, and more
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TLDR
The next generation of positioning models for positioning and data processing will depend on the design of the satellite itself, as well as on the satellite orbits it is placed in.Citations
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Journal ArticleDOI
Characterization of Cavities Using the GPR, LIDAR and GNSS Techniques
Miguel Angel Conejo-Martín,Tomás Ramón Herrero-Tejedor,J. J. Lapazaran,Enrique Pérez-Martín,Jaime Otero,Juan F. Prieto,Jesus Velasco +6 more
TL;DR: In this paper, the authors present a complete study of the joint use of ground-penetrating radar (GPR), LIDAR and GNSS techniques in the characterization of cavities.
Journal ArticleDOI
An Improved QZSS Satellite Clock Offsets Prediction Based on the Extreme Learning Machine Method
TL;DR: An improved clock prediction method combining the spectrum analysis model (SAM) and extreme learning machine (ELM) is proposed with abbreviation as iELM and is verified to be effective for the GPS/QZSS constellation as positioning accuracy is improved.
On the Robustness of Next Generation GNSS Phase-only Real-Time Kinematic Positioning
Lennard Huisman,Dennis Odijk +1 more
TL;DR: In this article, the robustness of multi-frequency real-time kinematic (RTK) models for phase-only RTK processing has been investigated and compared with the traditional phase+code RTK model.
Book ChapterDOI
Disaster Monitoring and Management
TL;DR: In the last century, the frequency, severity and impact of natural disasters has increased substantially as mentioned in this paper and the frequency and severity of such disasters have varied in type, frequency, coverage and severity ranging from earthquakes, landslides, droughts, floods, tornadoes, hurricanes, tsunamis, volcanic eruptions.
Book ChapterDOI
Micro-scale Landslide Displacements Detection Using Bayesian Methods Applied to GNSS Data
Francesco Pirotti,Alberto Guarnieri,Andrea Masiero,Carlo Gregoretti,Massimo Degetto,Antonio Vettore +5 more
TL;DR: In this article, the authors evaluate the movement of 6 points near a landslide body, which were surveyed with GNSS receivers over time, and apply Bayesian inference to identify the areas on the ground with statistically significant vertical (downwards) shifts.