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342 results for “GNSS”
Dataset for the article Development of a Submillimetric GNSS-Based Distance Meter for Length Metrology
<p>The data files contain the GNSS RINEX observation files and the ASCII files of the EDM measurements for the different measurement campaigns used in the article "Development of a Submillimetric GNSS-Based Distance Meter for Length Metrology" by Luis García-Asenjo, Sergio Baselga, Chris Atkins and Pascual Garrigues, Sensors 2021, 21, 1145. https://doi.org/10.3390/s21041145. This work was partly performed within the 18SIB01 GeoMetre project of the European<br> Metrology Programme for Innovation and Research (EMPIR). This project has received funding<br> from the EMPIR programme co-financed by the Participating States and from the European Union’s<br> Horizon 2020 research and innovation programme, funder ID: 10.13039/100014132. This research was also partly funded by the<br> Spanish Ministry of Education, Culture and Sports (PRX17/00371).</p> <p>All files are ASCII files: RINEX files with standard extension and filename notation, where PL1A indicates pillar 1 GNSS with receiver A, PL3B pillar 3 GNSS with receiver B, (as indicated in the paper two receivers per pillar where used), etc.; and space-separated ASCII files with explanatory headers for the ME5000 EDM observations.</p>
Kalman filter-based integration of GNSS and InSAR observations for local non-linear strong deformations
<p>The published database is related to the calculations presented in the paper "Kalman filter-based integration of GNSS and InSAR observations for local non-linear strong deformations". The main catalogue contains three folders named as GNSS, Campaign, and DInSAR.</p> <p>In the GNSS folder, the time series of XYZ coordinates and uncertainties estimated in the post-processing scenario for RES1, PI02, PI03, PI04, PI05, and PI16 permanent stations are provided. The GNSS calculations were performed at the Wrocław University of Environmental and Life Sciences in the ITRF2014 reference frame.</p> <p>The Campaign folder contains the results of epoch-based GNSS measurements and was used in the article as a verification data source. The Campaign results, prepared by the Military University of Technology, were used in the quality analyses. In order to co-locate the permanent PI02, PI04, PI05, and PI16 receivers with the nearest campaign points, a cross-reference was performed. The epoch-based time series of XYZ coordinates and uncertainties are provided in the ITRF2014 reference frame.</p> <p>The DInSAR interferograms were prepared at the Wrocław University of Environmental and Life Sciences and the results were stored in two directories named as Ascending and Descending. To perform a point-based unification of DInSAR and GNSS techniques, it was necessary to acquire the data from pixels intersected by the GNSS permanent station's locations. The DInSAR time series contain displacements (DSP), incidence angles (INC_ANG), heading angles (HEAD_ANG), and coherence (COH) data.</p>
GNSS training - dataset - Rybnik- 1st GATHERS Summer School
<p>The files in the collection contain a observation data, results and visualization algorithms. Data were collected during 1st GATHERS Summer School in Rybnik (<a href="https://www.kopalniaignacy.pl/en/">Ignacy Historic Mine</a>). The observations were taken at the Ignacy historical mine: <a href="https://goo.gl/maps/DEGFyDW5c3BfXDDR8">Location in Google Maps</a></p> <p>Following files are stored:</p> <p>GATHERS-T.5.1.1-Summer-School-GNSS-Observations-Results-PU.zip file contains following folders:</p> <p>- GNSS_data_Rybnik_2022-09-22 - RINEX files (GNSS receivers and smartphone) from field measurements (shaking table, bicycle wheel, base station) as well as GNSS products, for the day of measurements, required for GNSS data processing and used during the 1st Summer School GNSS training session,</p> <p>- Processing_Results_PPP_RTKLib - RTKLib PPP estimated 3D kinematic time-series of coordinates in IGb14 reference frame, shaking table results are only provided (GNSS receiver),<br> <br> - Processing_Results_variometric_VADASE - VADASE variometricaly estimated 3D kinematic time-series of coordinates in IGb14 reference frame, shaking table results are only provided (GNSS receiver),</p> <p>- Processing_Resuklts_Baseline_RTKLib - GNSS kinematic, baseline estimated 3D kinematic time-series of coordinates in IGb14 reference frame, shaking table results are only provided (GNSS receiver),</p> <p>- MINISEED_shaking_table_Rybnik_2022-09-22 - accelerometer data from shaking table experiment in MINISEED format, Warning due to accelerometer short power outages accelerometer time series are not time-aligned with GNSS results</p> <p>GNSSseismoRybnik_2022-09-22.ipynb - Google Colaboratory file containing the Python code used during the 1st Summer School GNSS training. The code loads GNSS processing results as well as accelerometer files into Python ObsPy seismological environment for data visualization and analysis. </p> <p>READ_ME_FIRST.txt - explanation of Google Colaboratory paths handling<br> <br> 20220922_143919.mp4, 20220922_145315.mp4, 20220922_145616.mp4 - the video files demonstrating of shaking table experiment </p> <p>If some questions arise, please send a message to:</p> <p><a href="mailto:jan.kaplon@upwr.edu.pl">jan.kaplon@upwr.edu.pl</a>, </p>
ScintPi 2.0 and 3.0: low-cost GNSS-based monitors of ionospheric scintillation and total electron content
<p> </p> <p>This data set provides measurements made by PolaR5x and ScintPi3.0 receivers in Presidente Prudente, Brazil for two consecutive days.</p>
GNSS velocity fields in China and its vicinity under the Eurasian reference
<p>The dataset illustrates the GNSS velocities in China and its vicinity under the Eurasian reference used for calculating the strain rate field.</p>
Dataset: GNSS PPP-RTK Tightly Coupled with Low-cost Visual Inertial Odometry aiming at Urban Canyons
<p>GNSS can provide high-precision positioning worldwide and is the preferred positioning method in autonomous driving and intelligent transportation etc. However, in complex urban environments, due to serious signal occlusion, the positioning performance of GNSS deteriorates sharply, and it even provides incorrect positioning information. To obtain robust navigation, GNSS is usually integrated with inertial measurement unit (IMU) to form a GNSS/INS integrated system. But in complex scenarios, the performance of GNSS/INS is closely related to IMU. For low-cost MEMS IMU, though it can facilitate GNSS positioning to a certain extent, it still cannot stably provide reliable positioning information. Visual sensors and IMUs can be combined to form a VINS system, which can obtain accurate local pose estimation. Therefore, adding visual information to MEMS IMU-based GNSS/INS systems can effectively suppress the divergence of MEMS IMU errors, and provide precise and reliable positioning services in GNSS-challenging environments.</p>
GNSS data of ambient vibrations and artificial excitations of the Aaresteg bridge.
<p>GNSS Data:</p> <p>- Instrument: Java GrAnt-G3T antenna and Septentrio PolaRx receiver</p> <p>- sampling rate: 20 Hz</p> <p>- Bandwidth of loop filter: auto adjust</p> <p>- Date: 2021-09-27</p> <p>Location:</p> <p>Instrument was set up on the eastern railing of the Aaresteg bridge, 1.125 m height above the wooden planks. The bridge was excited by jumping, twisting, running, walking, impulse hammering and combinations thereof. </p>
Open Access on GNSS Permanent Networks Data in Case of Disaster
<p>Earthquakes, as a natural phenomenon causing large physical and social destruction, are the subject of intensive research throughout the world. Spurred by the fact that in year 2020, two catastrophic earthquakes hit Croatia, in March with epicenter near Zagreb and December with epicenter near Petrinja, at the Faculty of Geodesy, University of Zagreb activities were initiated with the aim of strengthening the ability to react in these situations. Focus of those activities is on providing fast, adequate, and complete information on the disaster in the field of geodesy and geoinformatics. The research was focused on interpretation of kinematics of surface motion during the earthquake itself for what high rate permanent GNSS (Global Navigation Satellite System) network stations registrations are necessary. The Croatian earthquakes experience as well as the Mexico (June 2020) and Samosa earthquake (October 2020), pointed out, related to the use of high-rate registration GNSS data, that the primary problem in the use of this data is open access to the data itself. That is why this study has been launched - to gain a global picture of the availability of data from permanent GNSS networks around the world. The research included the collection and processing of information on open access policies for permanent GNSS networks data in the event of natural disasters with an emphasis on earthquakes. A global survey of institutions around the world responsible for managing GNSS permanent networks has been conducted. The survey contains three groups of questions that include general information on the type of permanent networks, models of access to network data and the readiness of countries to reach an international agreement on the opening data of the GNSS network in the event of a disaster. The results indicated that a high percentage of countries participating in the survey were ready to agree to open the data and introduce a common international portal through which scientists and researchers would be able to download GNSS permanent network data free of charge in the event of natural disasters.</p>
VIO-GNSS Dataset: Benchmarking Dataset for Sensor Fusion of Visual Inertial Odometry and GNSS Positioning
<p>This upload contains datasets for benchmarking and improving different Sensor Fusion implementations/algorithms. The documentation for these datasets can be found on <a href="https://github.com/AaltoVision/vio-gnss-dataset">GitHub</a>.</p> <p>The upload contains two datasets (version 1.0.0):</p> <ul> <li>urban_with_gnss_dead_zones (7.0 GB, ~16 minutes) <ul> <li>City streets</li> <li>A building is passed through on two occasions which makes the GNSS location signal unavailable at times.</li> <li>RTK Fix is acquired at times</li> </ul> </li> <li>suburban_nature (10.6 GB, ~19 minutes) <ul> <li>The route begins on a suburban street but quickly turns into a nature trail. Lots of vegetation</li> <li>The RTK solution is only Float or None most of the route.</li> </ul> </li> </ul> <p>Details on collecting the data:</p> <ul> <li>Software <ul> <li>The data was collected using <a href="https://github.com/AaltoVision/vio-gnss-recorder">this</a> open-source recorder. <ul> <li>Can be easily replayed using <a href="https://github.com/SpectacularAI/sdk-examples">SpectacularAI's SDK</a> (sdk-examples/python/oak/vio_replay.py)</li> </ul> </li> <li>Each dataset contains a map of the travelled route in Otaniemi, Espoo, Finland.</li> <li><strong>Necessary files to implement SLAM are included</strong> in the dataset.</li> <li>Use of NTRIP and the high precision GNSS antenna enables global positioning accuracy of only few centimeters.</li> </ul> </li> <li>Hardware <ul> <li>OAK-D stereo depth + color camera (Luxonis)</li> <li>C099-F9P GNSS module (u-blox)</li> <li>ANN-MB-00 high precision GNSS antenna (u-blox)</li> </ul> </li> </ul>
Research data of ISR and GNSS observation
<p>This is data used for a submitted article to an AGU journal ( ISR and GNSS data).</p>
The Dynamics of the India-Eurasia Collision: Faulted Viscous Continuum Models Constrained by High-Resolution Sentinel-1 InSAR and GNSS Velocities
<p>Velocity field for the India-Eurasia collision zone from Sentinel-1 InSAR and GNSS data</p> <p>Citations:</p> <p>[1] Jin Fang, Gregory A Houseman, Tim J Wright, Lynn A Evans, Tim J Craig, John R Elliott and Andy Hooper (2023). The Dynamics of the India-Eurasia Collision: Faulted Viscous Continuum Models Constrained by High-Resolution Sentinel-1 InSAR and GNSS Velocities [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.10053499</p> <p>[2] Jin Fang, Gregory A Houseman, Tim J Wright, Lynn A Evans, Tim J Craig, John R Elliott and Andy Hooper (2024). The Dynamics of the India-Eurasia Collision: Faulted Viscous Continuum Models Constrained by High-Resolution Sentinel-1 InSAR and GNSS Velocities, Journal of Geophysical Research: Solid Earth, https://doi.org/10.1029/2023JB028571</p> <p>More details about the methodology to generate the velocity field can be found in Wright et al. (2023):</p> <p>[3] Tim J Wright, Greg Houseman, Jin Fang, Yasser Maghsoudi, Andy Hooper, John Elliott, Lynn Evans, Milan Lazecky, Qi Ou, Barry Parsons, Chris Rollins, Lin Shen, Hua Wang (2023). High-resolution geodetic strain rate field reveals dynamics of the India-Eurasia collision, submitted to Science, preprint available at https://doi.org/10.31223/X5G95R.</p>
GNSS-IR data for "Real-time water levels using GNSS-IR: a potential tool for flood monitoring"
<p>Organised SNR data used for GNSS-IR analysis in the article "Real-time water levels using GNSS-IR: a potential tool for flood monitoring" by David Purnell, Natalya Gomez, William Minarik and Gregory Langston.</p><p> </p><p>The directories 'rv3s' and 'sjdlr' contain SNR data corresponding to sites Trois-Rivières and Saint-Joseph-de-la-Rive, respectively.</p><p> </p><p>Software for processing the data can be found at: https://github.com/purnelldj/gnssir_rt</p><p> </p><p>SNR data is given as text files in the format specified here except for columns 4+:</p><p>https://gnssrefl.readthedocs.io/en/latest/pages/file_structure.html#the-snr-data-format</p><p>columns:</p><p>1. sat PRN with an offset such that GLONASS satellites are between 100-200 and Galileo are between 200-300</p><p>2. satellite elevation (degrees)</p><p>3. azi is satellite azimuth (degrees)</p><p>4. GPS time (seconds since 1980)</p><p>5. L1 SNR</p>
Reprocessing of Three-Decade GNSS Observations: Millimeter-Level Global Velocity Field and Plate Motion Model Refinement
<p>The accurate and reliable Terrestrial Reference Frame (TRF) functions as a unified spatiotemporal datum crucial for solid earth research, encompassing disciplines such as geodesy and geodynamics. High-precision GNSS velocity field products stand out as pivotal foundational data for the maintenance of the TRF. This dataset encapsulates the outcomes of two GNSS velocity field refinement products: Global GNSS Velocity Model 2020 (GGVM2020) and the Global Interpolation Velocity Model 2020 (GIVM2020). GGVM2020 comprises velocity values and formal errors derived from over 3000 GNSS sites worldwide. And GIVM2020 incorporates speed values from approximately 2000 grid points on land globally, with a grid spacing of 3 degrees.</p>
Comparative Analysis of Methods to Estimate Geodetic Strain Rates from GNSS Data in Italy
<p>This dataset comprises GNSS velocity field and strain rate maps for Italy.</p> <p><strong>List of Files:</strong></p> <ol> <li> <p><strong>velocity_dataset.dat</strong></p> <ul> <li>GNSS velocity field of stations with time series longer than 4.5 years.</li> <li>Columns: Longitude (degrees), Latitude (degrees), East component of velocity (mm/yr), North component of velocity (mm/yr), Uncertainty on the East component (mm/yr), Uncertainty on the North component (mm/yr), Station Name.</li> </ul> </li> <li> <p><strong>velocity_dataset_filtr.dat</strong></p> <ul> <li>Filtered velocity field.</li> <li>Columns: Longitude (degrees), Latitude (degrees), East component of velocity (mm/yr), North component of velocity (mm/yr), Uncertainty on the East component (mm/yr), Uncertainty on the North component (mm/yr), Station Name.</li> <li>Stations ending with 'GPM' represent velocity values obtained by merging neighboring stations.</li> </ul> </li> <li> <p><strong>strain_rate_nn.dat (strain_rate_visr.dat, strain_rate_wav.dat)</strong></p> <ul> <li>Strain rate computed on cells spaced by 0.025°.</li> <li>Suffixes in the file names: 'nn' refers to the Nearest Neighbor method, 'visr' refers to the VISR method, and 'wav' refers to the Wavelet-based method</li> <li>Columns: Longitude (degrees), Latitude (degrees), exx (east) component of the strain rate tensor (nstr/yr), exy (east, north) component of the strain rate tensor (nstr/yr), eyy (north) component of the strain rate tensor, second invariant of the strain rate (nstr/yr), most extensive eigenvalue (nstr/yr), most compressive eigenvalue (nstr/yr), angle between north and the direction of the eigenvector corresponding to the most compressive eigenvalue (degrees, positive clockwise).</li> </ul> </li> </ol> <p><strong>Reference:</strong></p> <p>For further details, please refer to the article "Comparative Analysis of Methods to Estimate Geodetic Strain Rates from GNSS Data in Italy", published in <em>Annals of Geophysics</em>.</p>
Displacement time series from GNSS stations in the Alto Tiberina Fault area (Central Italy)
<p>The files report the position time-series of GNSS stations deployed in the Alto Tiberina Fault area (Central Italy). <br>Columns are: Time, E, N, Se, Sn, Ren, U, Su, Reu, Rnu, site, long, lati, representing, respectively, epoch (in decimal years), displacement in the East component (in mm), displacement in the North component (in mm), uncertainty (one standard deviation) of the East component (in mm), uncertainty (one standard deviation) of the North component (in mm), correlation between the East and North components, displacement in the Up component (in mm), uncertainty (one standard deviation) of the Up component (in mm), correlation between the East and Up components, correlation between the North and Up components, Station ID (four letters), Longitude of the station (°), Latitude of the station (°).</p>
Daily GNSS time series of La Palma 2021 eruption
<p>Time series form GNSS station in La Palma (Canary Island, Spain) during 2021 eruption (19/09/2021-21/01/2022). The daily neu time series have been computed in a Double Diference method using Bernese v.5.2 software as describe in the references.</p> <p> </p>
Detrended and corrected GNSS data from Lake Taupō, New Zealand, 2014–2021
<p>If using this data please cite the following publication:</p> <p>Schuler, J., Hreinsdóttir, S., Illsley-Kemp, F., Holden, C., Townend, J., Villamor, P. The response of Taupō Volcano to the M7.8 Kaikōura Earthquake. <em>Journal of Geophysical Research - Solid</em> <em>Earth. </em>2024.</p> <p>Also refer to this publication for full details on the methods used to derive the dataset. </p>
Calculated GNSS offsets for the 2016 Kaikōura earthquake
<p>Calculated offsets for the entire New Zealand GNSS network for the 2016 Kaikōura earthquake. If using this data please cite the following paper:</p> <p>Clark, K.J., Nissen, E.K., Howarth, J.D., Hamling, I.J., Mountjoy, J.J., Ries, W.F., Jones, K., Goldstien, S., Cochran, U.A., Villamor, P. and Hreinsdóttir, S., 2017. Highly variable coastal deformation in the 2016 MW7. 8 Kaikōura earthquake reflects rupture complexity along a transpressional plate boundary. <em>Earth and Planetary Science Letters</em>, <em>474</em>, pp.334-344.</p>
Datasets for "Effects of equatorial plasma bubbles on multi-GNSS signals: A case study over South China"
<p>Post-processed airglow data for Figure 1 and source data for other Figures.</p>
The raw GNSS position time series (raw_time_series.rar) and TDEFNODE models (TDEFNODE_models.rar) related to the manuscript authored by Rui Xu, D. S. Stamps and C. A. Williams
<p>This repository saves the raw GNSS position time series (raw_time_series.rar) and TDEFNODE models (TDEFNODE_models.rar) related to the manuscript authored by Rui Xu, D. S. Stamps and C. A. Williams. For more details, please refer to the NOTES files in each .rar archive.</p>
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