Investigation of Different Interpolation Models Used in Network-RTK for the Virtual Reference Station Technique

Al-Shaery, A.; Lim, Samsung; Rizos, Chris

doi:10.5081/jgps.10.2.136

Cited by 32 publications

(18 citation statements)

References 16 publications

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“…The Real Time Kinematic (RTK) method's positioning accuracy depends on the inter-station distances of the GNSS networks. The troposphere delay error reaches a few cm at distance of 70 km and above between the reference stations [11].…”

Section: Introductionmentioning

confidence: 95%

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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Section: Introductionmentioning

confidence: 95%

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

Selbesoglu

2019

Applied Sciences

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“…The NRTK positioning is a technology that calculates the correction message suitable for an area where a GPS rover is positioned [2,3]. The calculated correction message is transferred to the rover, to enable highly accurate real-time positioning [4]. Multiple GPS reference stations are utilized, because using a single reference station reduces accuracy if the distance between the rover and the reference station exceeds 10 km [5,6].…”

Section: Introductionmentioning

confidence: 99%

Development of an RTK-GPS Positioning Application with an Improved Position Error Model for Smartphones

Hwang

Yun

Suh

et al. 2012

Sensors

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This study developed a smartphone application that provides wireless communication, NRTIP client, and RTK processing features, and which can simplify the Network RTK-GPS system while reducing the required cost. A determination method for an error model in Network RTK measurements was proposed, considering both random and autocorrelation errors, to accurately calculate the coordinates measured by the application using state estimation filters. The performance evaluation of the developed application showed that it could perform high-precision real-time positioning, within several centimeters of error range at a frequency of 20 Hz. A Kalman Filter was applied to the coordinates measured from the application, to evaluate the appropriateness of the determination method for an error model, as proposed in this study. The results were more accurate, compared with those of the existing error model, which only considered the random error.

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“…On the basis of numerous comparisons of interpolation model applications in the network solution as presented in e.g. (Dai et al, 2004;Geisler, 2006;Próchniewicz, 2011;Al-Shaery et al, 2011), the Ordinary Kriging Method (OKR) may be indicated as the optimal interpolation method for distancedependent errors in regional network, which is characterized as highly precise in determining correction and makes possible the proper estimation of their variance. Numerical tests on positioning model solutions as presented in this paper applied the OKR method, utilizing the gaussian theoretical variogram model.…”

Section: Application Parameters and Solution Algorithmmentioning

confidence: 99%

“…Distance-Based Linear Interpolation Method (Gao et al, 1997), Linear Combination Model (Han andRizos, 1996, 1997), Linear Interpolation Method (Wanninger, 1995), Low-Order Surface Model (Fotopoulos and Cannon, 2001)) and (ii) the geostatistical methods (e.g. Least-Squares Collocation Method (Marel, 1998;Raquet, 1998), Ordinary Kriging Method (Wielgosz et al, 2003;Al-Shaery et al, 2011)). From among these models, only Distance-Based Linear Interpolation Method does not allow the defining of the predicted values errors.…”

Section: Application Parameters and Solution Algorithmmentioning

confidence: 99%

Study on the influence of stochastic properties of correction terms on the reliability of instantaneous network RTK

Próchniewicz

2014

Artificial Satellites

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ABSTRACT. The reliability of precision GNSS positioning primarily depends on correct carrier-phase ambiguity resolution. An optimal estimation and correct validation of ambiguities necessitates a proper definition of mathematical positioning model. Of particular importance in the model definition is the taking into account of the atmospheric errors (ionospheric and tropospheric refraction) as well as orbital errors. The use of the network of reference stations in kinematic positioning, known as Network-based Real-Time Kinematic (Network RTK) solution, facilitates the modeling of such errors and their incorporation, in the form of correction terms, into the functional description of positioning model. Lowered accuracy of corrections, especially during atmospheric disturbances, results in the occurrence of unaccounted biases, the so-called residual errors. The taking into account of such errors in Network RTK positioning model is possible by incorporating the accuracy characteristics of the correction terms into the stochastic model of observations. In this paper we investigate the impact of the expansion of the stochastic model to include correction term variances on the reliability of the model solution. In particular the results of instantaneous solution that only utilizes a single epoch of GPS observations, is analyzed. Such a solution mode due to the low number of degrees of freedom is very sensitive to an inappropriate mathematical model definition. Thus the high level of the solution reliability is very difficult to achieve. Numerical tests performed for a test network located in mountain area during ionospheric disturbances allows to verify the described method for the poor measurement conditions. The results of the ambiguity resolution as well as the rover positioning accuracy shows that the proposed method of stochastic modeling can increase the reliability of instantaneous Network RTK performance.

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Investigation of Different Interpolation Models Used in Network-RTK for the Virtual Reference Station Technique

Cited by 32 publications

References 16 publications

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

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

Development of an RTK-GPS Positioning Application with an Improved Position Error Model for Smartphones

Study on the influence of stochastic properties of correction terms on the reliability of instantaneous network RTK

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