Modelling the temporal variation of the ionosphere in a network-RTK environment
Creators: name: Wyllie, Scott
Publisher: name: RMIT University
Year: 2024

description: The Global Positioning System (GPS) has been widely used for precise positioning applications throughout the world. However, there are still some limiting factors that affect the performance of satellite-based positioning techniques, including the ionosphere. The GPS Network-RTK (NRTK) concept has been developed in an attempt to remove the ionospheric bias from user observations within the network. This technique involves the establishment of a series of GNSS reference stations, spread over a wide geographical region. Real time data from each reference station is collected and transferred to a computing facility where the various spatial and temporal errors affecting the GNSS satellite observations are estimated. These corrections are then transmitted to users observations in the field. As part of a Victorian state government initiative to implement a cm-level real time position ing service state-wide, GPSnet is undergoing extensive infrastructure upgrades to meet high user demand. Due to the sparse (+100km) configuration of GPSnet's reference stations, the precise modelling of Victoria's ionosphere will play a key role in providing this service.<br><br>This thesis aims is to develop a temporal model for the ionospheric bias within a Victorian NRTK scenario. This research has analysed the temporal variability of the ionosphere over Victoria. It is important to quantify the variability of the ionosphere as it is essential that NRTK corrections are delivered sufficiently often with a small enough latency so that they adequately model variations in the ionospheric bias. This will promote the efficient transmission of correctional data to the rover whilst still achieving cm-level accuracy. Temporal analysis of the ionosphere revealed that, during stable ionospheric conditions, Victoria's double differenced ionospheric (DDI) bias remains correlated to within +5cm out to approximately two minutes over baselines of approximately 100km. However, the data revealed that during more disturbed ionospheric conditions this may decrease to one minute.<br><br>As a preliminary investigation, four global empirical ionospheric models were tested to assess their ability to estimate the DDI bias. Further, three temporal predictive modelling schemes were tested to assess their suitability for providing ionospheric corrections in a NRTK environment. The analysis took place over four seasonal periods during the previous solar maximum in 2001 and 2002. It was found that due to the global nature of their coefficients, the four global empirical models were unable to provide ionospheric corrections to a level sufficient for precise ambiguity resolution within a NRTK environment.<br><br>Three temporal ionospheric predictive schemes were developed and tested. These included a moving average model, a linear model and an ARIMA (Auto-Regressive Integrated Moving Average) time series analysis. The moving average and ARIMA approaches gave similar performance and out-performed the linear modelling scheme. Both of these approaches were able to predict the DDI to +5cm within a 99% confidence interval, out to an average of approximately two minutes, on average 90% of the time when compared to the actual decorrelation rates of the ionosphere. These results suggest that the moving average scheme, could enhance the implementation of next generation NRTK systems by predicting the DDI bias to latencies that would enable cm-level positioning.<br>