The purpose of this thesis is to do quality assessment of GNSS/IMU derived NAP heights for The Netherlands (NAP) using Fugro RILA technique and the RDNAPTRANS2018 published by Rijkswaterstaat. The use of Global Navigation Satellite System (GNSS) is growing rapidly in order to determine the position (both horizontal and vertical). However, the GNSS is only able to give the geometric height which is the position on the ellipsoid, which have a drawback that the surface of constant ellipsoidal height are not equipotential surface, and hence these heights need to be transformed into traditional height systems such as the Normaal Amsterdam Peil (NAP) used in The Netherlands. In order to derive these NAP heights from the GNSS heights, a (quasi-) geoid model along with corrector surface model is used to convert from one height to another. In this study the newly computed local quasi geoid model NLGEO2018 rather than the NLGEO2008 is adapted using RDNAPTRANS2018 along with Fugro Rail Infrastructure aLignment Acquisition (RILA) technique which makes use of GNSS/IMU to obtain ellipsoidal height. It was found upon using the older transformation procedure RDNAPTRANS2008, which makes use of NLGEO2008, a mismatch in the order of 17mm between the NAP and GNSS-derived NAP from RILA. This error is not only due to the geoid itself but also the systematic errors in the different height system. However, this new geoid NLGEO2018 along with the new transformation procedure of RDNAPTRANS2018 allows a more accurate conversion of ellipsoidal to normal heights and this study focuses on this quality assessment of the new GNSS/IMU derived NAP heights and investigate how well NAP heights be obtained using RILA technique based on the new geoid. It was found with the use of the new geoid and the new transformation model RDNAPTRANS2018, the mean height difference was reduced from 12mm to 3.6mm showing a better fit of GNSS heights to NAP heights. An error budget was also calculated, to understand the reliability of these RILA measurements with the NAP heights. This error budget includes all the uncertainties from all error sources, namely RILA derived height, geoid height and the levelled height, followed by hypothesis testing. The highest uncertainty was from GNSS/IMU measurements from RILA system followed by the levelling and then the gravimetric quasi-geoid. Finally from this analysis, areas where the terrestrial surveying can be avoided was found. It was found that near the stations, tunnels, high vegetation had the highest uncertainty due to poor GNSS reception. It was also found that level crossing also showed a constant systematic offset between the two heights potentially due to the materials of the level crossings.

In this contribution, we extend the principle of integer bootstrapping (IB) to a vectorial form (VIB). The mathematical definition of the class of VIB-estimators is introduced together with their pull-in regions and other properties such as probability bounds and success rate approximations. The vectorial formulation allows sequential block-by-block processing of the ambiguities based on a user-chosen partitioning. In this way, flexibility is created, where for specific choices of partitioning, tailored VIB-estimators can be designed. This wide range of possibilities is discussed, supported by numerical simulations and analytical examples. Further guidelines are provided, as well as the possible extension to other classes of estimators.

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Guaranteeing safety of navigation within the Netherlands Continental Shelf (NCS), while efficiently using its ocean mapping resources, is a key task of Netherlands Hydrographic Service (NLHS) and Rijkswaterstaat (RWS). Resurvey frequencies depend on seafloor dynamics and the aim of this research is to model the seafloor dynamics to predict changes in seafloor depth that would require resurveying. Characterisation of the seafloor dynamics is based on available time series of bathymetry data obtained from the acoustic remote sensing method of both single-beam echosounding (SBES) and multibeam echosounding (MBES). This time series is used to define a library of mathematical models describing the seafloor dynamics in relation to spatial and temporal changes in depth. An adaptive, functional model selection procedure is developed using a nodal analysis (0D) approach, based on statistical hypothesis testing using a combination of the Overall Model Test (OMT) statistic and Generalised Likelihood Ratio Test (GLRT). This approach ensures that each model has an equal chance of being selected, when more than one hypothesis is plausible for areas that exhibit varying seafloor dynamics. This ensures a more flexible and rigorous decision on the choice of the nominal model assumption. The addition of piecewise linear models to the library offers another characterisation of the trends in the nodal time series. This has led to an optimised model selection procedure and parameterisation of each nodal time series, which is used for the spatial and temporal predictions of the changes in the depths and associated uncertainties. The model selection results show that the models can detect the changes in the seafloor depths with spatial consistency and similarity, particularly in the shoaling areas where tidal sandwaves are present. The predicted changes in depths and uncertainties are translated into a probability risk-alert map by evaluating the probabilities of an indicator variable exceeding a certain decision threshold. This research can further support the decision-making process when optimising resurvey frequencies.

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Accurate weather forecasting plays an important role in predicting precipitation events. With the warming climate the precipitable water vapour in the atmospheric is increasing. Since weather parameters as precipitable water vapor have a high spatial variability, interpolation of water vapor data over an area of hundreds of kilometer does not have a sufficient quality for weather prediction applications. Nowadays, researchers are investigating if the precipitable water vapour can be quantified using GPS transmitted signals in a densified GPS network. An accurate quantification of the ionospheric delay is important to efficiently calculate the precipitable water vapour. Moreover, the ionospheric delay is the biggest error and limitation of the GPS signal. It is important to understand how the ionospheric delay varies spatially and in time. Therefore, variability in the ionospheric delay is an interesting factor in weather forecasting and climate change. To monitor the ionospheric delay a high temporal (in minutes) and spatial resolution (in km-grid) is needed, because the ionospheric delay changes spatially and throughout the day. A possibility to achieve this is to densify GPS networks. Previous research has shown that it is possible to measure the ionospheric delay with dual frequency receivers. In developing countries this densification of GPS networks cannot be achieved with expensive dual-frequency receivers. This study investigates if a higher receiver network density can be achieved with the help of low-cost single frequency receivers. Therefore, a densified GPS network of dual and single frequency receivers is set-up in and around Kampala, Uganda. This research demonstrates how the Satellite-specific Epoch-difference Ionospheric Delay model (SEID) can be used to compute the ionospheric delay for a single frequency receiver through time. The SEID model creates a second frequency for a single frequency receiver which is used to resolve the ionospheric delay. The intensity of the ionospheric delay depends on the electrons in the ionosphere. The number of free electrons in the path of a signal is expressed as the total electron content. This research shows how to compute the total electron content in the ionospheric layer of the atmosphere. After computing the second frequency for the single frequency receivers the observations need to be processed using Precise Point Positioning (PPP) to compute the precipitable water vapour. As a case study Uganda is chosen, because it is located on the equator. The ionospheric delay fluctuates more at the equator so this is an interesting region to investigate the variability. The analysis shows that an high accuracy of the GPS signal is needed to create desirable results. Therefore, field campaigns with single frequency and dual frequency receivers should incorporate antennas with noise reduction. In order tot assess the accuracy of the ionospheric delay obtained by using single and dual frequency receivers, future research should be focus on better network set-up and getting the right equipment with better noise reduction

In the Krafla region in North East Iceland, a change in the deformation pattern was observed in 2018. Since the 1975-1984 rifting event, there was subsidence seen in the Krafla caldera and along the fissure swarm. This subsidence was explained by deflation which decreased exponentially along the years. In 2018, this deformation pattern reversed, and for the first time since 1989, uplift was observed in the region. Therefore, this work concentrates on examining the change in deformation pattern in the region that occurred in 2018. Four geodetic techniques, a combination of ground and space geodetic techniques, are used to study this change in deformation pattern noted in the Krafla region. Data from 2016 to 2019 are considered as they span the time of interest when the change took place. In levelling, InSAR and GPS, the uplift observed is located to the North of the Krafla power plant. Similarly, there is an increase in net gravity seen to the North of the power plant. This suggests there is an increase in mass/density. A possible reason could be movement of magma from the deep to the shallow magma chamber. The reason behind this start of the magma movement is not known. On the other hand, there is continued subsidence and an increase in net gravity seen in the fissure swarms present to the south of the power plant. This result agrees with the fact that the uplift is concentrated (a local phenomena) in the northern region and there is still subsidence seen in the south. A simple Mogi model is fitted to InSAR and GPS data to understand the cause for this change in deformation. But, the fitted model provided very few insights in this work. The results from modelling mainly concentrated on the subsidence in the southern region and corresponding model parameters were obtained. The location of the source responsible for the modelled deformation (subsidence) is found to be near the power plant. The depth and volume however are not properly constrained. But since the model tries to constrain at deeper depths, correspondingly larger volume is also seen. This outcome strengthens the hypothesis that the source for the deformation could lie at deeper depths. Based on the overall results, the following recommendations are made to enhance the results: i) Maintaining the continuity in the data ii) To increase the number of benchmarks measured near the deformation area (especially in gravity and levelling) iii) Use of multiple source modelling for the better understanding of the sub-surface processes.

Gathering accurate and reliable precipitation data is essential in developing countries, since it applied to a wide range of applications, from improving short term weather models to global climate change research. The most common way of acquiring rainfall measurements is the rain gauge. However, this traditional measurement equipment requires frequent visits from researchers, due to the clogging risks as well as the risk of being easily tampered with by unauthorized people. This makes it impractical and expensive to create a extensive network of these rain gauges, whilst the demand for the precipitating data remains high. A measurement equipment type which is more suitable for this remote and independent requirement for precipitation measurement in developing countries is GPS, since an aspect of GPS measurements is the possibility to use the equipment in relatively remote conditions, with little human interference necessary. In addition to this, due to the nature of GPS measurements, rain is expected to be an important component in GPS data, as a disruptive to the signal, a variable found in the Signal-to-Noise ratio (SNR). Considering the above, GPS seems like a good alternative to the traditional rain gauge for precipitationmeasurements. During this additional thesis, the question whether GPS can be used reliably for precipitation data, will be answered. During a measuring campaign in Uganda from September to November in 2018, GPS data was gathered near precipitation measurement locations from TWIGA’s School-2-School initiative. For the processing, graphs of the SNR and precipitation of the same days and locations were created and were visually inspected to see if a relationship or correlation was present. From these graphs this relationship between SNR and precipitation was not immediately clear. The nature of the SNR can be caused by a multitude of reasons and variables and unless the correlation is very strong between variables, this correlation will not be very clear from just a visual inspection of these SNR and precipitation graphs. To untangle these variables, correlationmatrices were used, where the variables can be looked at in pairs and instead of as quadruplets (or more). From the correlation matrix of the Ugandan data is is clear that correlation between precipitation and SNR is not significant, as it is similar to the correlation with a randomly generated variable. A slight correlation is present, however, with the GPS elevation angle. During processing, data from Cabauw (the Netherlands) was used as an extra data set to compensate for the small amount of rain in the Ugandan data. This processing followed the same procedure as was applied with the Ugandan data; visual inspection of the SNR and precipitation graphs and generating a correlationmatrix. In the Dutch data the relationship between the SNR and the precipitation was still small, however, larger than in the Ugandan data. Nonetheless, the correlation between the SNR and the elevation was very strong. In conclusion, from both the Ugandan and Dutch data, the correlation between precipitation and SNR is not strong enough, or in other words, using GPS data to approximate rainfall, is not achievable using the methods applied and resources used during this additional thesis.

This research focuses on GNSS users interested in a faster position fix in a situation where the receiver holds limited data. This occurs when turning on a mobile phone or sport watch to use a location-based service, driving out of a parking garage with a navigational device or activating a search & rescue beacon. In these cases, users care more about directly setting a certain action into motion, than waiting until a sub-meter level of accuracy is acquired. For this purpose, we aim to decrease the time to first fix (TTFF) of a receiver at the expense of position accuracy by focusing on its most dominant component: the time to read the first fix data, i.e. the TTFFD. In turn, the TTFFD is decreased by reducing the size of the clock correction and ephemeris data (CED) and integrating this reduced set into the existing Galileo I/NAV message while maintaining backward compatibility. Three reduction strategies are presented to reduce the size of the CED. First, the expression of the CED parameters relative to the matching almanac parameters, which requires downloading the most recent almanac. Second, the absorption of the clock and ephemeris time reference into related parameters by leveraging redundancies in the user algorithm. Third, the truncation of the CED parameters, where the optimal parameter bit allocation per CED size is determined by means of a Monte Carlo simulation. Subsequently, the optimal reduced set is integrated in the existing Galileo I/NAV message, since it is not only size but also the location and repetition rate of the reduced CED in the navigation message that determine the final decrease in TTFFD. A simulation is developed where the three reduction strategies are combined and applied to one year of Galileo navigation messages and almanacs starting from October 2017. The current I/NAV message has a size of 428 bits and an average TTFFD of 25.4 seconds. The relative expression of the CED parameters, the absorption of the clock time reference and ephemeris time reference yield a 76, 15 and 10 bit reduction respectively, without a significant decrease in position accuracy expressed as the signal-in-space ranging error (SISRE). The reduction in size that can be realized by truncation results from a trade-off with the maximum acceptable level of SISRE. When combining all three reduction strategies a 296 bit, i.e. 69%, size reduction can be realized at a SISRE level of 10.04 meters, yielding a 132 bit CED. 66% of the size reduction is in this case achieved by parameter truncation. To maintain backward compatibility, the spare and reserved bits of the I/NAV message are used to integrate the reduced set. This finally results in an average TTFFD of 6 seconds, which is a decrease of almost 20 seconds. With the reduced CED the user gains full independence of out-of-band channels for the computation of a fast position fix. In addition, it decreases the impact of bit errors on the TTFF and increases the chance of receiving an error free set of first fix data. Moreover, the reduced CED is not only of interest to fast fix applications that desire a decrease in waiting time. Positioning based on the reduced set also decreases the time the receiver has to be "on" to read the navigation data and consequently the receiver's power usage. This leads to a second type of applications that benefits from the reduced CED: low-power applications that do not require a high accuracy, such as freight management, wildlife tracking and stolen goods trackers. To conclude, the constructed reduced CED provides multiple advantages for a wide range of GNSS users and should therefore be considered as a purpose for the spare and reserved bits of the Galileo I/NAV message.

Measurements of a tachymetric network in the geothermal area of Bjarnarflag in North-East Iceland are performed during four consecutive years (2015-2018). For the yearly adjustment of the measurements an alternative iteration scheme to Baarda’s ’B-method of testing’ is proposed, because the level of significance of the F-test performed in the detection step is too large due to the large redundancy of the measurements. The proposed alternative method detects outliers and verifies the stochastic model simultaneously, resulting in 3
to 4% rejected measurements per year. From the yearly solutions absolute and relative horizontal velocities are estimated. The relative velocities are significant, but have large uncertainties. Adding three years of data reduces the mean standard deviation of the estimated velocities from 2.4 mm in east-direction to 0.9 mm and 1.8 mm in north-direction to 0.8 mm. The improvement of precision can be accelerated by reevaluating the stochastic input parameters, adding more GNSS measurements and reconsidering the network design.
Improved horizontal relative velocities can be used to understand the horizontal deformation patterns in the area of study due to extraction of water or steam by the geothermal powerplant, due to the instability of the benchmaks or due to natural processes.