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.

Geodesists use multiple methods to monitor surface deformation. This gives the opportunity to integrate complementary data for better interpretation of deformation processes. The integration of data has to deal with: (1) spatio-temporal differences in sampling methodologies, meaning that observed objects are not the same and thus observed processes may stem from different sources; and (2) physical differences in the methodologies leading to different kinds of coordinate reference systems with possibly different datums. This makes integration of data challenging.In this thesis, we address one of the fundamental roots to this integration problem, by developing co-located reference points for multiple geodetic monitoring methods. This will ensure that, for multiple geodetic monitoring methods, one common deformation process is observed. This eliminates interpolation between observations. I show the main requirements for an Integrating Geodetic Reference Station (IGRS), present a design for a fully functional IGRS and describe its performance. Furthermore, the protocols for deployment and operational use are given. Benchmarks for InSAR, GNSS, levelling, LiDAR, and gravimetry are present on the IGRS. From field tests, it is found that presented radar reflectors have a 1-sigma measurement precision of <0.5 mm in the Line-of-Sight of the radar. This includes both the structural stability as the influence due to clutter. Furthermore, it is shown that the GNSS antenna has mm precise performance, similar to standard monitoring GNSS set-ups.The developed IGRS can be deployed to create a local datum connection between datasets. Ideally IGRSs, either the design presented in this thesis or similar, are deployed where integration, validation and calibration of data, especially InSAR data, is needed.

Nowadays, many people and organizations depend on Global Navigation Satellite Systems (GNSS. Monitoring of GNSS is important to ensure the quality of GNSS measurements and products. The availability of the signals-in-space (SiS) is an essential part of the monitoring of GNSS, but it is not clear how availability is defined and standards for monitoring are lacking. The main research question for this thesis is therefore: Which are the key performance indicators related to availability that unambiguously describe sensor station and system performance in time, how can these be computed in an operational manner, and how can they be presented in a condensed form to the stakeholders? This includes an objective of defining unambiguous performance parameters for sensor station and system, and address the considerations related to the definition. A prototype software tool is created to study the algorithms and compute the key performance indicators.

Availability is in the basis a binary operation: a signal is available or unavailable. When this is applied on daily measurements, daily statistics can be computed. A signal is considered available if the code, carrier phase and C/N0 measurements are present, and meet certain standards. A signal is said to be expected if the satellite is expected to transmit that signal, the receiver is configured to receive that signal, and the signal is not blocked by objects in the signal’s path to the sensor station. For this it’s needed to define and compute an elevation mask for each station.

The sensor station and system performance parameters are computed from a network of sensor stations, using files in the Receiver Independent Exchange format (RINEX). Four key performance indicators are defined for the sensor station performance. The Daily Station Availability describes the part of the day that the station is operational, the Daily Station Total Availability gives how well the station receives, and the Effective Mean Elevation quantifies the elevation mask and thus the location of the sensor station. These three parameters are summarized into the Overall Station Quality parameter, which gives and overall performance class to the sensor station.

For the system performance a satellite is considered available if all signals are received by a sensor station of the monitoring network and the health status is healthy. The satellite is considered unavailable if the signals are received by none of the monitoring stations, while expected by at least two stations, or the health status is unhealthy. The Daily GNSS Availability parameter gives the percentage of the day that the satellite was available and the Daily Available number of Satellites tells how many satellites were available during the day. 

The parameters are computed for a period of 100 days. Results are presented using color codes and by showing only detailed information in case of anomalies or specific investigations. The proposed key performance indicators showed to be very useful at pointing out good performances or anomalies. While SiS availability gives much insight in the performance, a monitoring tool can be improved when combined with other performance aspects.

 

Precise Point Positioning (PPP) is a Global Navigation Satellite System (GNSS) processing method with the objective of providing high positioning accuracy without the need for a nearby base station or dense network of reference stations operated by the user. To reach this objective, PPP uses the very precise carrier phase measurements in addition to the coarse pseudorange measurements, and precise satellite orbits and clock offsets. Before the carrier phase measurements can contribute to the position solution, the carrier phase biases must be estimated from the measurements. Additionally, to compute an accurate position, a number of approaches are used in PPP to reduce the error contributions. A sparse global network of reference stations estimates very accurate satellite orbits and clock offsets, and provides these to the user. A priori models are used to correct for the hydrostatic troposphere delay, relativistic effects, carrier phase wind-up, phase center offsets and variations. We will mainly consider users on or close to the surface of the Earth, which means that site displacement effects (e.g. ocean loading and solid Earth tides) also should be taken into account. The troposphere wet delay is often estimated from the data, and, in conventional PPP, the ionosphere delays are eliminated by combining dual-frequency observations in a reduced number of ionosphere-free observations. The accuracy provided by PPP based on GPS is very high; just a few mm for 24h of static data. However, PPP can still be improved in a number of areas which are addressed in this dissertation: - The need to both eliminate the ionosphere delays and estimate the carrier phase ambiguities results in a relatively weak model. This leads to long (re)initialization times before an accurate position can be computed. In this dissertation a different approach is proposed, implemented and tested that does not form the ionosphere-free combination, but estimates the ionosphere-delays from the observations. First the (linearized) GNSS observation equations are introduced and the functional PPP model is derived. From this we determine the estimable parameters, and treat the possibility of constraining the ionosphere estimation by use of external ionosphere data. This is achieved by correcting the observations with ionosphere model values and estimating only the residual ionosphere delay. Finally, a dynamic model for the residual ionosphere delays is determined and positioning results are presented. - An accurate stochastic model for PPP is lacking, which leads to a suboptimal weighting of the different observation types, an inadequate quality description of the solution, and unreliable hypothesis testing. In this dissertation the stochastic properties of the PPP model are studied starting with the accuracy of the (real-time) satellite orbit and clock offsets, and continuing with the accuracy and correlation of the GNSS observations. For the latter a newly developed method is introduced based on the geometry-free model. This analysis also includes an estimation procedure to model pseudorange multipath errors as an auto-regressive process, which can also be used to construct a dynamic model for time-correlated parameters. Finally, an almost unbiased variance component estimation algorithm is applied to the complete PPP model, and an accurate quality description of the positioning results is obtained. - Integrity monitoring, which uses hypothesis testing to detect errors in the model which are not covered by the expected uncertainties, is not well developed for PPP. This subject is related to the previous points, since an accurate stochastic model is required for integrity monitoring and estimating and constraining the ionosphere delay, is key to high integrity performance. The geometry-free model is used again in time-differenced form to analyze the integrity aspects of (multi-frequency) GNSS models and their power to detect errors such as cycle-slips on the carrier phase measurements. The null hypothesis and alternative hypotheses are presented and the Minimal Detectable Bias (MDB) is derived for different kinds of errors. Analytical expressions are provided supported by numerical results. The geometry-free analysis of pseudorange and carrier phase measurement noise revealed a good agreement with the theoretical expressions linking the measurement noise to the receiver tracking loop parameters and signal-to-noise ratio. However, strong variations of the pseudorange code measurements over longer time periods were also observed, which dominate the error budget if not accounted for. Modeling of multipath time series as an autoregressive process AR(k) of order k, showed that the estimated AR(1) parameter is almost always significant to a very high level, which indicates strong time correlation of the multipath time series. Indeed the AR(1) model fits the data much better than a white noise or AR(0) model does, significantly reducing the residual variance. Analysis of the real-time precise products showed that compared to the satellite orbit prediction, satellite clock prediction is relatively poor and thus dominates the combined error. The quality of newer products with shorter delays is much higher and approaches the post-processed products. There also exists strong correlation between the orbit and clock products, resulting from the estimation process, but also due to the use of specific phase center offsets. Users should thus obtain the satellite orbit and clock products from the same provider and not mix products. Analysis of the time-differenced geometry-free model revealed the strong impact of constraining the ionosphere variations over time on the integrity performance. Instantaneous detection of individual slips in dual- and triple-frequency data was found to have a high probability of success under moderate ionosphere conditions. Detection of phase-slips in single-frequency data is more difficult and may lead to a delay in the detection. The size of the smallest pseudorange outliers which can still be detected is, except for single-frequency data, insensitive to the ionosphere conditions; the precision of the code measurements is the only determining factor. Testing for a simultaneous phase-slip on each frequency leads to a very elongated MDB ellipse or (hyper)ellipsoid if the ionosphere is not constrained, indicating that specific combinations of slips are very difficult to detect. Constraining the ionosphere delay shrinks the MDB ellipse when the precision of the ionospheric pseudo observable increases. The positioning results show that without proper modeling, the pseudorange observations cannot contribute significantly to the carrier phase solution without degrading the accuracy. Furthermore the formal quality description of the solution poorly fits the empirical position errors if time-correlated effects are neglected. After solving both these issues, the strength of the combined model was demonstrated by the excellent convergence performance in both the static and kinematic case. Constraining the atmosphere delays to model values further improves the initial convergence of the position estimation.@en