Global precise multi-GNSS positioning with trimble centerpoint RTX

Glocker, Markus; Landau, Herbert; Leandro, Rodrigo; Nitschke, M.

doi:10.1109/navitec.2012.6423060

Cited by 24 publications

(10 citation statements)

References 1 publication

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“…In this service, the products and necessary corrections, like precise satellite orbits and satellite clocks, ionospheric and tropospheric models, code and phase biases, and other auxiliary information, are computed via the Trimble's worldwide multi-GNSS tracking network. This network has approximately 120 globally distributed reference stations and they broadcast to the user via L-band geostationary satellites or cellular/internet connections (Glocker et al 2012). Nowadays, the system supports the GPS, GLONASS, Galileo, BeiDou and QZSS constellations.…”

Section: Introductionmentioning

confidence: 99%

“…A number of GNSS-related effects and corrections like antenna offset and directional variation corrections; satellite and receiver clock errors; tropospheric and ionospheric delays; site displacement effects; phase wind-up effects; relativistic effects; polar motion; code and carrier-phase biases and the others should be accounted in order to obtain global cm-level accurate positioning in Trimble CenterPoint RTX. The technical details and methodology on modelling of all these effects have been described in literatures such as Leandro et al (2011), Glocker et al (2012).…”

Section: Introductionmentioning

confidence: 99%

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Accuracy comparison of post-processed PPP and real-time absolute positioning techniques

Alkan

Erol

Ozulu

et al. 2020

Geomatics, Natural Hazards and Risk

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The main motivation of this study is to assess the accuracy performance of the real-time global GNSS PPP positioning service, i.e. Trimble CenterPoint RTX, and online PPP post-processing service, i.e. CSRS-PPP, in Çorum Province of Turkey. Within this scope, a geodetic point was established and GNSS data were logged in static mode at a measurement interval of 1 Hz. At the same time, the real-time coordinates of each measurement epoch were determined with the Trimble CenterPoint RTX positioning service using L-band geostationary satellite delivery. The collected GNSS data were also sent to the CSRS-PPP service and the coordinates of each measurement epoch were calculated. According to comparison of the coordinates obtained from CenterPoint RTX to the known coordinates, it was concluded that the 3D real-time positioning achieved at centimeter-level of accuracy within convergence time of a few minutes. On the other hand, CSRS-PPP also provided centimeter-level accurate positioning with post-processing mode. The overall results show that, the attainable accuracy with both approaches provided the requirements of the many scientific and practical surveying applications.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Accuracy comparison of post-processed PPP and real-time absolute positioning techniques

Alkan

Erol

Ozulu

et al. 2020

Geomatics, Natural Hazards and Risk

View full text Add to dashboard Cite

show abstract

“…These are the Digital Elevation Model (DEM), an RGB orthoimage, and monochromatic reflectance orthorectified images corresponding to each of the four channels. The post-processing was carried out through Trimble Real Time eXtended (RTX) correction services [39], a high-accuracy global GNSS correction technology that combines real-time data with positioning and compression algorithms. For RTX post-processed measurements, these dual-frequency geodetic instruments have a manufacturer's stated horizontal accuracy specification of up to 2 cm.…”

Section: Photogrammetric Algorithmmentioning

confidence: 99%

Evaluation of Fire Severity Indices Based on Pre- and Post-Fire Multispectral Imagery Sensed from UAV

et al. 2019

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Fire severity is a key factor for management of post-fire vegetation regeneration strategies because it quantifies the impact of fire, describing the amount of damage. Several indices have been developed for estimation of fire severity based on terrestrial observation by satellite imagery. In order to avoid the implicit limitations of this kind of data, this work employed an Unmanned Aerial Vehicle (UAV) carrying a high-resolution multispectral sensor including green, red, near-infrared, and red edge bands. Flights were carried out pre- and post-controlled fire in a Mediterranean forest. The products obtained from the UAV-photogrammetric projects based on the Structure from Motion (SfM) algorithm were a Digital Surface Model (DSM) and multispectral images orthorectified in both periods and co-registered in the same absolute coordinate system to find the temporal differences (d) between pre- and post-fire values of the Excess Green Index (EGI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Red Edge (NDRE) index. The differences of indices (dEGI, dNDVI, and dNDRE) were reclassified into fire severity classes, which were compared with the reference data identified through the in situ fire damage location and Artificial Neural Network classification. Applying an error matrix analysis to the three difference of indices, the overall Kappa accuracies of the severity maps were 0.411, 0.563, and 0.211 and the Cramer’s Value statistics were 0.411, 0.582, and 0.269 for dEGI, dNDVI, and dNDRE, respectively. The chi-square test, used to compare the average of each severity class, determined that there were no significant differences between the three severity maps, with a 95% confidence level. It was concluded that dNDVI was the index that best estimated the fire severity according to the UAV flight conditions and sensor specifications.

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“…receptor Trimble R6 working in the post-processed kinematic (PPK) mode and a wayside base station ( Figure 5). The post-processing of the measured coordinates of GCPs was undertaken by Trimble Real-Time eXtended (RTX) correction services [41]. This high-accuracy global GNSS technology combines realtime data with positioning and compression algorithms that can reach a horizontal accuracy of up to 2 cm.…”

Section: Survey Campaignmentioning

confidence: 99%

A Comparative Analysis of Phytovolume Estimation Methods Based on UAV-Photogrammetry and Multispectral Imagery in a Mediterranean Forest

et al. 2019

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Management and control operations are crucial for preventing forest fires, especially in Mediterranean forest areas with dry climatic periods. One of them is prescribed fires, in which the biomass fuel present in the controlled plot area must be accurately estimated. The most used methods for estimating biomass are time-consuming and demand too much manpower. Unmanned aerial vehicles (UAVs) carrying multispectral sensors can be used to carry out accurate indirect measurements of terrain and vegetation morphology and their radiometric characteristics. Based on the UAV-photogrammetric project products, four estimators of phytovolume were compared in a Mediterranean forest area, all obtained using the difference between a digital surface model (DSM) and a digital terrain model (DTM). The DSM was derived from a UAV-photogrammetric project based on the structure from a motion algorithm. Four different methods for obtaining a DTM were used based on an unclassified dense point cloud produced through a UAV-photogrammetric project (FFU), an unsupervised classified dense point cloud (FFC), a multispectral vegetation index (FMI), and a cloth simulation filter (FCS). Qualitative and quantitative comparisons determined the ability of the phytovolume estimators for vegetation detection and occupied volume. The results show that there are no significant differences in surface vegetation detection between all the pairwise possible comparisons of the four estimators at a 95% confidence level, but FMI presented the best kappa value (0.678) in an error matrix analysis with reference data obtained from photointerpretation and supervised classification. Concerning the accuracy of phytovolume estimation, only FFU and FFC presented differences higher than two standard deviations in a pairwise comparison, and FMI presented the best RMSE (12.3 m) when the estimators were compared to 768 observed data points grouped in four 500 m2 sample plots. The FMI was the best phytovolume estimator of the four compared for low vegetation height in a Mediterranean forest. The use of FMI based on UAV data provides accurate phytovolume estimations that can be applied on several environment management activities, including wildfire prevention. Multitemporal phytovolume estimations based on FMI could help to model the forest resources evolution in a very realistic way.

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Global precise multi-GNSS positioning with trimble centerpoint RTX

Cited by 24 publications

References 1 publication

Accuracy comparison of post-processed PPP and real-time absolute positioning techniques

Accuracy comparison of post-processed PPP and real-time absolute positioning techniques

Evaluation of Fire Severity Indices Based on Pre- and Post-Fire Multispectral Imagery Sensed from UAV

A Comparative Analysis of Phytovolume Estimation Methods Based on UAV-Photogrammetry and Multispectral Imagery in a Mediterranean Forest

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