Laser communication provides numerous benefits over typical Radio Frequency communication, such as lower power, not occupying regulated frequency bands, possibilities for much higher data rates and resistance to jamming. Combined with satellite constellations, Laser Inter-satellite Links (LISL) can enable global connectivity. However, satellites move at fast relative velocities, while optical beam divergence angles are in micro-radian levels. Thus, low-latency and precise position data between linking satellites is crucial. This thesis investigates the LISL conditions in a combined LEO/MEO constellation and the applicability of on-board GNSS-based Orbit Determination (OD) and Orbit Prediction (OP). Novel methods, such as Preprocessing Extended and Single-propagation Unscented Kalman Filters are tested and compared to typical GNSS-OD methods. Analyzing Pointing Uncertainty contributions in various link cases, results indicated that fully-kinematic methods could support LISL for 100-s periods, whereas reduced-dynamic OD-OP methods performed more consistently.

Absolute accuracy plays a crucial role in most geospatial applications. Particularly in the case of mobile mapping systems (MMS) where large amounts of data are collected, it comprises a key factor that can sometimes be dangerously underestimated. Within the scope of mobile laser scanning, the accuracy of the measurements is mainly ruled by the quality of the navigation solution. Commonly, as for the MMS that Fugro employs to capture 3D measurements from railway surroundings, called RILA, the trajectory of these mobile systems is determined by integrating data from positioning sensors, such as GNSS and IMU. Due to their complementary characteristics, the accuracy and robustness of the navigation solution can generally be considered sufficient. Nevertheless, in certain challenging environments where GNSS positioning conditions are limited, the trajectory results attained may not yet be accurate enough. Therefore, this study presents an alternative procedure to enhance the navigation solution of this MMS, making use of the point cloud data already acquired and georeferenced. Multiple runs or passes of the mapping system over the same problematic area take place at different times and some might possess satisfactory outcomes. Hence, after employing this dataset as reference, the quality of the others could be improved by means of point cloud matching methods. Next, the computed registration values are also applied to the trajectory, accordingly. Results over the area of interest selected show that applying Iterative Closest Point (ICP) using just a few feature points can already enhance the results. ICP iteratively finds the optimal rotation and translation to match two point clouds, minimising the differences between them. Furthermore, trajectory accuracies increase with the implementation of ICP utilising all points, without leading to extra processing times. However, GNSS/INS errors are not constant throughout time and space, which means that the application of a single rigid transformation may not be adequate. For that reason, an interpolated ICP-based method has been introduced and tested. An increasing number of point cloud sections was considered and among all, the solution with a relatively high number of sections performed better. This algorithm comprised a division of the point cloud data into 1000 contiguous sections or slices along the trajectory, each approximately 60 cm wide. The RMSE of the resulting absolute trajectory error and its standard deviation were greatly improved, with their values decaying from 41.1 cm and 18.1 cm to just 4.1 cm and 2.4 cm, respectively. In addition, provided a good reference dataset is available, the method proposed can be executed completely automatically and independently of the type of data captured and environment conditions. Even though these results might not yet reach the desired accuracy of around 1 cm to correctly georeference RILA’s LiDAR data, it has been proven that this method has the potential to yield much more accurate, consistent and reliable outcomes. Further research should be directed at generating an accurate reference dataset when none is initially available, by collectively and statistically combining the results of multiple surveys.

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.

Bathymetric surveying of the Netherlands Continental Shelf (NCS) is taken care of, by the Dutch Hydrographic Office and Rijkswaterstaat. The survey frequencies vary from location to location depending on a set of factors defined by the authorities. These are the minimum depth, draught (the difference between seafloor depth and ship’s keel), shipping intensity, human interventions and seafloor dynamics that are taking place in an area. The project is about developing a methodology using the available time-series of bathymetric data to automatically make a well-informed decision on the survey frequency of critical areas of the NCS. This study aims to contribute to the decision-making by performing a risk-based analysis using a probabilistic approach. The main objectives are to estimate and map the risk that the seafloor is changing significantly. Risk severity should be measured and the probability of risk's consequence to occur should be estimated and mapped in temporal and spatial scale indicating if possible, the optimal surveying frequency. Firstly, the accuracy, quality and reliability of the prediction values were defined and computed. This was a necessary step to realize how powerful are the predictions used as an input dataset for the risk assessment of the seafloor to change significantly. As a step further, the risk's impact and probability to occur should be determined. Hence, it was necessary to define quantities related to seabed dynamics that express the evolution of the seafloor. These are mentioned as risk indicators and are the Relative Depth Change (RDC), the Rate of Change (RoC) and the direction of change. The RoC was used for probability estimation and the rest of it as informative variables for the analysis. Finally, an adjustment of the method used here with the Netherlands Hydrographic Office (NLHO) standards was done and conclusions were made regarding the survey frequency of the chosen study areas. The analysis was applied for two study areas: West IJmuiden and West Rotterdam, as these areas according to the NLHO are highly prioritised (should be resurveyed every 2 years) and thus, they are critical areas of the NCS. The method applied and the results can be used as an informative tool in designing a successful, reliable and cost-effective bathymetric survey plan of the NCS.

Everything around us is rapidly changing. Whole new blocks of buildings are built, huge infrastructural projects are constructed and so on. Hence, there is a need of a reliable and up-to-date inventory of the area and the objects of interest for mapping and monitoring assets and their changes. An answer of this upcoming need is an automated inventory of infrastructure using Remote Sensing and artificial intelligence (AI) techniques. Rail sector shares the same need for fast and reliable inspection on its infrastructure. Monitoring frequently the condition of the railway infrastructure can improve the maintenance efficiency and the avoidance of hazards. The traditional monitoring techniques are costly, time consuming and in some cases dangerous, due to their reliance on the physical presence of the inspector. Hence, new state-of-the-art techniques that are able to frequently and without putting in risk human lives, inspect the condition of the railway and its infrastructure. This master thesis aims at developing an efficient workflow for combining 2D imagery and 3D light detection and ranging (LiDAR) point clouds for the automated detection and localization of the railroad infrastructural objects into 3D world coordinate system, for monitoring the railway infrastructure. Using deep learning (DL) methods in imagery we detected and mapped, approximately the 60% of the railroad equipment of our interest (i.e. light signals and equipment boxes). These detected equipment were analyzed with stereoscopic techniques to retrieve their position in 3D world coordinate system. That led to the automated creation of a geographical information system (GIS) map having the positional and class information of railway equipment. Once the detected objects were mapped, then the point cloud data were automatically cropped into voxels including the same objects. Hence, using various sophisticated machine learning (ML) techniques, the points referring to the objects were classified. Furthermore, combining the positional information provided via 2D analysis with 3D point clouds, the vertical position was refined and the height of the mapped objects was estimated. Lastly, the positional information estimated from the 2D analysis enhanced the unsupervised ML classification in point clouds. The product of this classification, has the potential to be used as training data to train supervised point cloud classifiers.

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.

The resiliency of coral reef islands to changing environments associated with climate change is controlled by the delicate balance between the import and export of sediment. The majority of the sediment is derived from coral reefs for which the stability of these islands is directly related to reef health. Understanding the sediment signature and its drivers is essential to assess island resiliency. We performed a study on sediments from the islands Eva and Fly in the Exmouth Gulf, Australia. We analysed the grainsize distribution and the abundance of sediment producers in order to desribe and discriminate the spatial distribution of these sediment characteristics and performed statistical analysis to identify corresponding key environmental drivers. We found that the sediments were typically course sand-sized (500 − 1000 [μm]) and the dominant constituent is reef-derived sediment. The median grainsize of Eva island (549 [μm]) is nearly equal to the median grainsize of Fly island (540 [μm]). The standard deviation of the grain size distribution of the sediments from Eva island was much larger than at Fly island. However, analysis of variance showed there were no significant differences between islands (Eva/Fly), hydrodynamic regimes (high/low), distance to shore (inshore/offshore) and local habitat (reef/no reef). Furthermore, a distance-based redundancy analysis showed no key environmental driver responsible for the distribution in grainsize and composition of the sediment. The environmental factors which were analyses were depth, temperature, pH, dissolved oxygen content and the oxidation-reduction potential. The spatiotemporal scales that were studied are potentially smaller than the scales on which climate change effects act, which explains the absence of significant spatial differences or key environmental drivers. Based on these findings it is not possible to assess the resiliency of Eva and Fly islands, however a study from Perry et al. (2011) found that islands with their particular characteristics (sand-sized and coral-dominated) are expected to undergo major morphological change under a range of predicted climate change scenarios. This research provides a baseline for future studies to assess the stability of Eva and Fly islands or sedimentological research other reef-derived islands.

In SAR interferometry, the data acquired by satellite are large. The points in the dataset are usually expressed in spatial-temporal dimension in which many epochs may be included. Extra information including contextual values and data from other sources sometimes are important to be taken into account when analyzing the deformation based on the time series. However, this kind of extra information is not supplied in the dataset. Here we show a way to augment the InSAR dataset by updating the new acquisitions, adding contextual values and combining data with other sources. We create an enriched dataset which provides one more knowledge on the points in the dataset, such that more accurate analysis and decision can be made. Also, a way to query a certain bunch of points with the same feature category is descripted, which allows one to find the points of interest based on their features. Our result provides a way to utilize multiple data sources and to combine them into an InSAR dataset in order to make the dataset be more efficiently used. The way depicted in this paper might be a hint of how to do a more accurate and comprehensive analysis based on a dataset. Furthermore, an automatic or semiautomatic way to modify the dataset with some tools are helpful.

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.

The Sentinel-5 Precursor satellite has a payload of the TROPOspheric Monitoring Instrument, TROPOMI.The satellite was launched in 2017 by ESA with the intended goal of measuring trace gases in the atmosphere.One of the products of TROPOMI is the Tropospheric NO2 column. This product is based on thespectral measurements to obtain the column abundance of NO2 in the troposphere. This product alsorelies on a-priori data and one of  these a-priori datasets is the albedo dataset.The currently used dataset has a resolution of 0.5°x 0.5°, which corresponds to approximately 55 kmx 34 km at mid-latitudes. The TROPOMI pixel size is significantly smaller, 3.5 km x 7 km. Due to this large difference in resolution the discussion arises if this used dataset is sufficient for accurate results. This researchmakes a comparison between the current a-priori dataset and possible replacements.
This paper makes this comparison by calculating Air Mass Factors (AMFs) using the OMI LERalbedo climatology as a reference and the two alternative high resolution surface reflectance datasets,Sentinel-2 and Landsat-8. These surface reflectance datasets were regridded and averaged on the corresponding TROPOMI grid. The focus area of this paper is the Greater Rotterdam region in the Nether-lands.Before these AMF calculations were done, a comparison between Sentinel-2 and Landsat-8 surfacereflectance datasets is made. This is done both on their own high resolution and regridded onto theTROPOMI grid. Above water surfaces and land covered by vegetation a bias of approximately 0.01 was present between the two high resolution surface reflectance datasets. These differences are relatively small. The differences calculated for the datasets regridded to the TROPOMI grid were also relatively small, with a bias of 0.01 above the water and vegetation surfaces.
Two cases were studied during this research: the 21st of April 2018 and the 6th/7th of May 2018. Theresults show that significant improvements can be made by using a higher resolution surface reflectancedataset. A median bias of -10.4% (-15.6%) was calculated for the 21st of April for Sentinel-2 (Landsat-8)compared to the AMFs based on the OMI albedo dataset. For May this was -3.9% (-9.3%). Furthermorethis study showed extreme AMF-biases of 68.0% overestimation and 39.8% underestimation by the OMIalbedo dataset compared to Sentinel-2, where the overestimation was observed over the greenhouses inthe Westland region and the underestimation in the rural region to the East of the domain in April.For May the underestimation was mostly observed to the West (North Sea), indicating that over regionswith a low surface reflectance the atmospheric correction greatly influences the AMF. The comparisonbetween Landsat-8 and OMI showed similar results in the AMF differences.These findings are supported further by a recent Sentinel-5P validation study, which comparedground based observations to the TROPOMI observations. This project found an NO2 underestimationof approximately 20% for many different stations. This research suggest that, at least partly, this difference can be explained by the coarse resolution of the a-priori albedo dataset used. 

The topic of this thesis is Integrity Monitoring (IM), with special focus on the Singapore Satellite Positioning Reference Network (SiReNT) infrastructure. This network infrastructure supports and improves Global Navigation Satellite System (GNSS) applications, related to positioning, navigation, tracking and (deformation) monitoring. This project focuses on what IM is with regards to the SiReNT infrastructure, and how the current methodology can be improved so that its output is easier to interpret/understand for its users. This research consists of two parts, a theory study, and a data study. The theory study is aimed at developing a fundamental understanding of GNSS and integrity, and the need for an IM component, as well as what comprises IM around the globe. The data study, which includes IM data processing, is performed to develop an understanding of the stability and performance of the SiReNT infrastructure itself, and its sensitivity to several degrading factors. For this the IM displacement data of a period of up to 5 months is analysed, in combination with the atmospheric information, and the number and geometry of tracked satellites. The Trimble method, with its default thresholds, has been applied to this IM displacement data, in order to determine what percentage was supposedly reliable, and inside these thresholds. It was also investigated whether these thresholds are performing adequately, if applied, and whether they have any impact on simultaneous recorded Rover data. The latter was assumed; the IM displacement data is thought to be usable to control the Rover data. When it comes to positioning solutions, the quality parameters include, integrity, accuracy, precision, availability, reliability, continuity, capacity and redundancy. These quality parameters are affected by several degrading factors. At the moment the Trimble Pivot Platform (TPP) is used to perform the network processing, as well as the integrity monitoring of the Continuous Operating Reference Stations (CORS) of the SiReNT infrastructure (and with that the impact of several of these degrading factors). This method is an example of the so-called external monitoring method. ‘External’ as the monitoring does not take place within the receiver itself, such as in the other method available; Receiver Autonomous Integrity Monitoring (RAIM). The external monitoring method and RAIM can either be used individually and/or independently, or in some sort of combination, complementing each other. In several other countries, namely the Netherlands, Australia (Victoria), Hong Kong, and Malaysia, variants of the external monitoring method are applied in performing IM of their GNSS infrastructures. Usually this includes baseline and coordinate processing/comparison, both in real-time and through post-processing (often using the Bernese GNSS software). All of them have backup systems in place. When it comes to IM visualization, even though there are similarities, the approaches differ per country. Some only visualise as much as the status of their reference stations, others visualise the real-time positioning solution of their IM stations, or have a complete dashboard visualising a number of parameters. From this research can be concluded that the integrity (in terms of CORS’ stability and positioning solution quality) of the SiReNT infrastructure is actually quite good. Of the IM displacement data which was supposedly reliable (following from the applied method), over 99% was within the set thresholds, for all CORS. The default thresholds (therefore) are iv

considered to be quite a good trade-off; they filter out most of the main disturbances (especially the ones which are assumed to be related to sudden severe fluctuations in the Ionospheric activity), while minimizing the amount of data loss. There is room for improvements however; as a start, the (overall) redundancy should be increased, making the SiReNT infrastructure more robust and increase its reliability. The method in which the IM is currently performed could be improved in several ways as well. This could for example be done by tracking more constellations/signals, constructing additional CORS and backup systems, and using multiple different hardware brands for the equipment that is used in the SiReNT infrastructure. Also, the SiReNT infrastructure should be monitored with respect to surrounding IGS stations, rather than using a CORS that is part of the SiReNT infrastructure itself as a reference. An important addition to the IM component could be the feedback from the Rover data and performance (the initialization time, and positioning solution quality). Currently made assumptions related to that do turn out to be erroneous, however. Any threshold applied to the IM displacement data cannot be used to control the Rover data. The way in which the output resulting from this IM is visualised, should be more intuitive/easy to interpret. For this a dashboard is to be developed (in design following the example of the dashboard of the Dutch Cadastre), which homepage should be visualizing several parameters, all in real-time. This would enable the (end-)user to see what is the current status with regards to all these parameters, in one view. One would however also be able to access further information related to these parameters, by selecting the one of interest. These parameters are to be the number of tracked satellites, the number of connected users, an indication of the Ionospheric delay, the predicted geometric error, and the Rover performance. The content of the information presented to the public should be kept to a minimum (only the necessary), where the content presented to the system administrators can be more elaborate. The conclusions of this research point towards the need for further research into how the applied methods/algorithms and thresholds (as well as the other available options) can be utilized in order to improve the IM procedure in Singapore. On top of that, the impact of interference should be identified and monitored. Being able to do this, will improve the (integrity of the) infrastructure and the information provided by it.

This paper analysis the effects of soil moisture and vegetation water content (VWC) on total backscattering (σ_tot^0) simulated by water cloud model (WCM) throughout a growth cycle of maize at different frequencies, polarization modes, and incidence angles. Firstly, the bare soil backscatter (σ_soil^0) was simulated by Integral Equation Method (IEM) surface scattering model [1] or Dubois empirical backscattering approach [2]. Then, to analysis the effect of vegetation cover, a standard and a double layer WCM based on parameter sets of three published studies [3]-[5] are applied in this study area to simulate the two components of σ_tot^0, direct backscatter from vegetated surface (σ_veg^0) and attenuated soil backscatter (〖γ^2 σ〗_soil^0). The input parameters of IEM, Dubois and WCM are supported by a series of ground measurements performed in Florida during the entire growth season, which includes soil moisture, surface roughness and vegetation biomass measurement. According to the analysis at different frequency and incident angle, the increase of bulk VWC can lead to either an increase or decrease in σ_(tot ). The different impact is determined by either σ_veg^0 or 〖γ^2 σ〗_soil^0 is the main contributor to σ_tot^0. At higher frequencies and larger incident angles, where the dominant part is from σ_veg^0, σ_tot^0 will increase with increasing bulk VWC. While, when 〖γ^2 σ〗_soil^0 becomes the main contributor to σ_tot^0, increasing bulk VWC leads to a denser canopy and thus more incoming microwave is attenuated. Therefore, increasing bulk VWC results in a decrease in σ_tot^0. Besides, according to the results obtained at C-band, different incident angle, HH-polarized microwaves are more sensitive to changes in bulk VWC, especially at larger incident angle. VV-polarization is less affected by vegetation cover, σ_tot^0is sensitive to soil moisture even at peak biomass and large incidence angles, which is attributed to scattering along the soil-vegetation pathway.

Global Navigation Satellite System (GNSS) has been developed in recent several decades, which provides a new technique for timing, positioning and navigation. And GNSS positioning is a basic service to us, both in daily life and scientific research. During high-precise positioning, ambiguity resolution is a key factor that has a huge influence on the accuracy of the result. And the wrong fixing is themain source of lose of accuracy, so many methods of test are proposed to validate the fixing result. We are interested in the performance of solutions that have been labeled as wrong fixing, and want to check if those wrong fixings should be excluded or accepted during estimation.
At firstwe introduce the basic model and challenges of ambiguity fixing, aswell as the widely usedmethod integer least squares and Z-transformation. Some previous research of distribution of fixed solution also enables us to compute the bounds of baseline residual. Then we focus on an example to do some pre-research, and find out the main research question - how to accept wrong fixing during estimation and the impact under different scenarios or estimation methods. We use a simulation-basedmethod to do the research but the
measurements are generated based on the real ephemeris in certain day.
After giving the double difference measurement model based on code and phase with respect to single or dual frequencies, in short baseline scenario, we define some parameters 1-norm, infinity-norm, weighted 2-norm, and good/bad performance of wrong fixings that might be useful for analysis. And some detailed information in chosen epochs are shown with multiple figures and we derive those which are helpful, such as infinity-normratio. Then we develop 4 validation methods, Infinity-normratio detection (RD),Weighted 2-norm detection (WD), 1-norm baseline residual detection (BD1) and Infinity-norm baseline residual detection (BDi), to check if we could recognize wrong fixings with good performance from all wrong fixings.
We also compute some statistics, such as the success rate of detection, the rate of misdiagnose, and the rate of participation to see whether our validation methods are effective or not. The histogram of wrong fixing or correct fixing corresponds the existed research of distribution well. And the time series analysis also proves the reliability of our validation methods, although there exist some errors due to the small sample size of simulations.
We also apply the multi-epoch least squares and Kalman filter to see the influence of fixing success rate, standard deviation of horizontal residuals, residual bounds and performance of wrong fixings. We find that there are obvious improvements on all of them. An extra experiment is designed to see the impact of atmospheric delays and the results show that it is really different from short baseline scenarios and we need to find more proper threshold to make sure our validation methods work.
Finally, we could draw a conclusion that it is possible to find out wrong fixings that could be accepted, but the threshold for each validation method should be adaptive for different scenarios and Kalman filter is very reliable, with which the wrong fixing is always likely to be excluded during the estimation.

Since GPS tends to fail for indoor positioning purposes, alternative methods like indoor positioning systems (IPS) based on Bluetooth low energy (BLE) are developing rapidly. Generally, IPS are deployed in environments covered with obstacles such as furniture, (partition-) walls, people and electronics influencing the signal propagation. The major factor influencing the system performance and to acquire optimal positioning results is the geometry of the beacons. The geometry of the beacons is limited to the available infrastructure that can be deployed (number of beacons, basestations and tags), which is dependent on the budget and deployment effort of the customer.
This leads to the following challenge: Given a limited number of beacons, where should they be placed in a specified indoor environment, such that the geometry contributes to optimal positioning results?

This challenge is approached by using theoretical design computations. The design computations require the definition of a chosen 3D space, the number of beacons, possible user tag locations and a performance threshold (e.g. required precision). For any given set of beacon and receiver locations, the precision, internal- and external reliability can be determined on forehand. The results of a given geometry have been validated by deploying an IPS of BlooLoc and comparing the observed precision with the modeled precision for a chosen set of user tag locations. The theoretical model showed a precision pattern with several equivalent precision patterns of the measured data, however, some significant differences could not be explained physically and the model has to be adapted further. Besides determining the precision based on a set of beacon and receiver locations, the model is able to select the optimal geometric configuration based on a performance threshold (e.g. required precision). Depending on the performance threshold, the optimal configurations can either be a single solution or consist of multiple solutions that satisfy the performance threshold of the customer. The performance threshold varies depending on the use case and the user requirements. Therefore, the amount of possible combinations in terms of 3D space, amount of available beacons, possible beacon locations, user tag locations and performance thresholds are limitless and the model can thus be used for all kind of applications.

All in all, the initial model (design computations) can be used by IPS customers for all kind of applications. The model is able to select the optimal geometric configuration in terms of precision based on a performance threshold specified by the user. Furthermore, the model requires user specified input parameters and the amount of possible combinations are therefore unlimited. Although the initial model has to be adapted further to account for the differences in modeled and measured data as a consequence of environmental factors, the initial model is a good initiative for the rising indoor positioning market and its customers. Therefore, the model can be adapted further such that it can explain significant differences between the modeled and the measured data by including factors that influence the system performance in real life, such as materialistic properties, signal attenuation, interference, multipath, NLOS etc.