The VRS network-based technique has become the main precise GNSS surveying method especially for medium-range baselines (approximately 20-70 km). The key concept of this approach is to use the observables of multiple reference stations to generate the network correction in the form of a virtual reference station for mitigating distance-dependent errors including atmospheric effects and orbital uncertainty at the user’s location. Numerous GNSS data processing strategies have been adopted in the functional model in order to improve both the positioning accuracy and the success of ambiguity resolution. However, it is impossible to completely model the aforementioned errors. As a result, the unmodelled residuals still remain in the virtual reference station observables when the least squares estimation is employed. An alternative approach to deal with these residuals is to construct a more realistic stochastic model whereby the variance-covariance matrix is assumed to be homoscedastic. This research aims to investigate a suitable stochastic model used for the VRS technique. The rigorous statistical method, MINQUE has been applied to estimate the variance-covariance matrix of the double-difference observables for a virtual reference station to rover baseline determination. The findings of the comparison to the equal-weight model and the satellite elevation-based model indicated that the MINQUE procedure could enhance the positioning accuracy. In addition, the reliability of ambiguity resolution is also improved.
The key concept of the virtual reference station (VRS) network-based technique is to use the observables of multiple reference stations to generate the network corrections in the form of a virtual reference station at a nearby user’s location. Regarding the expected positioning accuracy, the novice GNSS data processing strategies have been adopted in the server-side functional model for mitigating distance-dependent errors including atmospheric effects and orbital uncertainty in order to generate high-quality virtual reference stations. In addition, the realistic stochastic model also plays an important role to take account of the unmodelled error in the rover-side processing. The results of our previous study revealed that the minimum norm quadratic unbiased estimation (MINQUE) stochastic model procedure can improve baseline component accuracy and integer ambiguity reliability, however, it requires adequate epoch length in a solution to calculate the elements of the variance-covariance matrix. As a result, it may not be suitable for urban environment where the satellite signal interruptions take place frequently, therefore, the ambiguity resolution needs to be resolved within the limited epochs. In order to address this limitation, this study proposed the stochastic model based on using the residual interpolation uncertainty (RIU) as the weighting schemes. This indicator reflects the quality of network corrections for any satellite pair at a specific rover position and can be calculated on the epoch-by-epoch basis. The comparison results with the standard stochastic model indicated that the RIU-weight model produced slightly better positioning accuracy but increased significant level of the ambiguity resolution successful rate.