Accuracy investigation of height obtained from Classical and Network RTK with ANOVA test

Gumus, Kutalmis; Selbesoglu, Mahmut Oguz; Çelik, Cahit Tağı

doi:10.1016/j.measurement.2016.04.045

Cited by 17 publications

(8 citation statements)

References 11 publications

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“…However, Gumus et al (2016) found that 'classical' single base RTK levels were more accurate than NRTK. It appears that the 'classical' RTK in Gumus et al's study was a base-rover setup.…”

Section: Literature Reviewmentioning

confidence: 94%

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Assessing Quality of Corsnet-NSW Infrastructure for Use in Regional New South Wales, Australia

McMahon

Paudyal

2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. CORSnet-NSW is a network of Global Navigation Satellite System (GNSS) Continuously Operating Reference Stations (CORS) covering the state of NSW and providing centimetre-level real-time positioning. This research paper aims to determine the level of accuracy and precision of CORSnet-NSW in regional parts of the network and compare them to that of traditional RTK under identical conditions. It investigates what effects satellite geometry and proximity to a CORS station have on NRTK measurements. It also verifies the CORSnet-NSW claim of network wide 2cm accuracy. The claim was found to be true at the 68% Confidence Interval. Changes in satellite geometry were not found to affect the results for the most part. It was also found that CORSnet-NSW precision was affected by the proximity of the rover to the base/calibration points more than its proximity to a CORSnet-NSW station. However, even though the CORSnet-NSW results were better than expected, traditional RTK remains the most precise and consistent method. This research paper provides GNSS users in regional NSW with the evidence they need to make informed decisions regarding which type of RTK method is fit for their purposes.

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“…However, Gumus et al (2016) found that 'classical' single base RTK levels were more accurate than NRTK. It appears that the 'classical' RTK in Gumus et al's study was a base-rover setup.…”

Section: Literature Reviewmentioning

confidence: 94%

“…Studies comparing the vertical accuracy of NRTK to traditional RTK have been performed in various European countries such as Latvia (Dobelis & Zvirgzds 2016), Spain (Garrido et al 2011) and Turkey (Gumus et al 2016). However, as errors are network dependent the results of these studies cannot be applied to the CORSnet-NSW network.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing Quality of Corsnet-NSW Infrastructure for Use in Regional New South Wales, Australia

McMahon

Paudyal

2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…and to send the corrections to long distances in real time. This system consists of 3 or more stable stations that make continuous observations [13,14]. NRTK necessitates an ambiguity resolution "engine" to fix integer ambiguities in static multiple reference receivers in stations with known positions to form the network [9].…”

Section: Methodsmentioning

confidence: 99%

Examining the Accuracy of Network RTK and Long Base RTK Methods with Repetitive Measurements

et al. 2019

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Real-time kinematic (RTK) technique is important for mapping applications requiring short measure time, the distance between rover and base station, and high accuracy. There are several RTK methods used today such as the traditional RTK, long base RTK (LBRTK), network RTK (NRTK), and precise point positioning RTK (PPP-RTK). NRTK and LBRTK are popular with the advantage of the distance, the time, and accuracy. In the present study, the NRTK and LBRTK measurements were compared in terms of accuracy and distance in a test network with 6 sites that was established between 5 and 60 km. Repetitive NRTK and LBRTK measurements were performed on 6 different days in 2015-2017-2018 and additionally 4 campaigns of repetitive static measurements were carried out in this test network. The results of NRTK and LBRTK methods were examined and compared with all relevant aspects by considering the results of the static measurements as real coordinates. The study results showed that the LBRTK and NRTK methods yielded similar results at base lengths up to 40 km with the differences less than 3 cm horizontally and 4 cm vertically.

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“…In the linear interpolation method, the coefficients a and b are determined according to the least squares method by Equation (12).…”

Section: Linear Interpolation Methods (Lin)mentioning

confidence: 99%

“…In addition, tropospheric delay can cause errors in the height component up to several cm for RTK positioning [12]. The error increases when the regional meteorological conditions notably change, and differences up to 3-4 cm may occur, depending on the season.…”

Section: Introductionmentioning

confidence: 99%

Spatial Interpolation of GNSS Troposphere Wet Delay by a Newly Designed Artificial Neural Network Model

Selbesoglu

2019

Applied Sciences

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Global Navigation Satellite System (GNSS) signals arrive at the Earth in a nonlinear and slightly curved way due to the refraction effect caused by the troposphere. The troposphere delay of the GNSS signal consists of hydrostatic and wet parts. In particular, tropospheric wet delay prediction and interpolation are more difficult than those of the dry component due to the rapid temporal and spatial variation of the water vapor content. Wet delay estimation and interpolation with a sufficient accuracy is an important issue for all parameters obtained by GNSS positioning techniques. In particular, in real-time positioning applications, errors caused by interpolation of the wet troposphere delay are reflected in the height component of about 1 to 2 cm. Furthermore, the amount of water vapor in the troposphere is very important information in weather forecast applications obtained as a function of wet delay. Therefore, real-time monitoring of the troposphere can be achieved with a higher resolution and accuracy by utilizing neural network models for interpolation of the wet tropospheric delay. In addition, in the absence of the GNSS station, wet delays can be interpolated by means of the surrounding stations to the desired location. In this study, a back propagation artificial neural network (BPNN) model based on meteorological parameters obtained from The New Austrian Meteorological Measuring Network (TAWES) was used to interpolate wet troposphere delay. Analysis was carried out at 40 reference stations of the Echtzeit Positionierung Austria (EPOSA) GNSS Network covering the whole of Austria. The interpolation of zenith wet delays based on the artificial neural network was performed by using latitude, longitude, altitude and meteorological parameters (temperature, pressure, weighted mean temperature, and water vapor pressure). These parameters were then subtracted from the artificial neural network model one by one and six different artificial neural networks were designed. In addition, the linear interpolation method (LIN) and inverse distance weighted interpolation method (IDW) were used as conventional interpolation methods. In order to investigate the effect of the network density on interpolation methods, three networks, including 40, 30, and 20 reference stations, were formed and the increased distance effect on interpolation methods was evaluated. In addition, analyses were conducted in winter, spring, and summer to evaluate the seasonal effects on interpolation methods. According to the statistical analysis, the root mean square error (RMSE) values of the IDW, LIN, and BPNN methods were found to be 12.6, 13.4, and 5.9 mm, respectively.

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Accuracy investigation of height obtained from Classical and Network RTK with ANOVA test

Cited by 17 publications

References 11 publications

Assessing Quality of Corsnet-NSW Infrastructure for Use in Regional New South Wales, Australia

Assessing Quality of Corsnet-NSW Infrastructure for Use in Regional New South Wales, Australia

Examining the Accuracy of Network RTK and Long Base RTK Methods with Repetitive Measurements

Spatial Interpolation of GNSS Troposphere Wet Delay by a Newly Designed Artificial Neural Network Model

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