Sidereal filtering for multi-GNSS precise point positioning and deformation monitoring

Abstract

For earthquake and tsunami early-warning, it is crucial that displacements resulting from earthquakes are recorded with speed and accuracy. Traditional methods based on seismometer data often suffer from errors during integration which results in the maximum displacement not being accurately recorded. In contrast, Global Navigation Satellite Systems (GNSS) can measure permanent static displacement directly; however it too is subject to errors, the main error of which is multipath. Multipath can lead to errors in the measurement of small displacements or mask the displacement completely. Multipath is dependent on the geometry of the GNSS constellation orbits and the antenna’s surrounds. GPS satellites have an orbital period of half a sidereal day with a near-sidereal repeating ground track. Similarly, the GLONASS constellation geometry repeats about once every eight sidereal days thus the satellite-reflector geometry will repeat with these same periods. By accurately determining the repeat periods it is possible to remove the multipath error by analysing data from the previous repeat periods. This method is known as sidereal filtering and can be used to improve the precision of GNSS coordinate time series and hence improve displacement measurements. This thesis looks to find the optimum geometry repeat period for the GLONASS constellation, which was found to be 689248 s and combine GPS and GLONASS for observation domain near-sidereal filtering. GLONASS-only filtering improves GLONASS coordinate solution standard deviations, on average, by 22.3%, 18.1% and 17.6% in the East, North and Up, whereas GPS and GLONASS combined filtering improves GPS and GLONASS standard deviations by 21.2%, 23.4% and 25.1%. The average maximum stability improvement, in terms of Allan deviation for all components is approximately 21.0% for GLONASS-only and 29.0% for combined filtering. Combined filtering produces more stable coordinate time series for averaging intervals over a few hundred seconds. It also reduces coordinate time series standard deviations and thus aids the measurement of small coordinate displacements and reduces the number of false alarms by half during displacement detection. Filtering improves the accuracy and precision of displacement estimates on average by about 2 mm, in terms of the difference between filtered and unfiltered RMSD and mean displacement values.