Multi-frequency and multi-GNSS PPP phase bias estimation and ambiguity resolution

Multi-frequency and multi-GNSS measurements from modernized satellites are properly integrated for PPP with ambiguity resolution to achieve the state-of-the-art fast and accurate positioning, which provides an important contribution to GNSS precise positioning and applications. The multi-frequency and multi-GNSS PPP phase bias estimation and ambiguity resolution, which is accomplished by a unified model based on the uncombined PPP, are thoroughly evaluated with special focus on Galileo and BDS

Multi-frequency and multi-GNSS PPP phase bias estimation and ambiguity resolution

Multi-frequency and multi-GNSS measurements from modernized satellites are properly integrated for PPP with ambiguity resolution to achieve the state-of-the-art fast and accurate positioning, which provides an important contribution to GNSS precise positioning and applications. The multi-frequency and multi-GNSS PPP phase bias estimation and ambiguity resolution, which is accomplished by a unified model based on the uncombined PPP, are thoroughly evaluated with special focus on Galileo and BDS

Ionospheric Regional modeling Algorithm based on GNSS Precise Point Positioning

Precise point positioning (PPP) is an absolute spatial positioning technology different from carrier phase relative positioning. With the continuous development of Global navigation satellite system (GNSS), multi-constellation GNSS further provides PPP with more abundant observation information and useful spatial geometric observations, which improves positioning performance and robustness. In recent years, the un-difference and un-combined precise point positioning (UPPP) has been continuously developing. Firstly, we introduce the basic theory of GNSS positioning and compare the position performance between UPPP and ionospheric-free PPP (IF PPP). The positioning performance of the four mainstream GNSS systems, GPS, GLONASS, Galileo, and Beidou, the PPP floating-point solutions of the four satellite systems all converge within 60 minutes and their error are less than 10cm. Secondly, a two-dimensional (2-d) model is proposed to fit the vertical total electronic content (VTEC) in the ionosphere with the ionospheric delays extracted by UPPP. With the model constraining the ionospheric delay in UPPP, the convergence is 2 minutes shorter than using the global ionospheric map (GIM) from IGS. Thirdly, to solve the limitation of the traditional methods in 2d representation, a method is proposed represent the ionosphere in 3D, called Compressed Sensing Tomography (CST). Comparing the simulated single-difference slant total electron content (STEC) and the input single- difference STEC between satellites, the root mean square (RMS) of the reference station’s error is less than 1 TEC uni

Benefits from a multi-receiver architecture for GNSS precise positioning

Precise positioning with a stand-alone GPS receiver or using differential corrections is known to be strongly degraded in an urban or sub-urban environment due to frequent signal masking, strong multipath effect, frequent cycle slips on carrier phase, etc. The objective of this Ph.D. thesis is to explore the possibility of achieving precise positioning with a low-cost architecture using multiple installed low-cost single-frequency receivers with known geometry whose one of them is RTK positioned w.r.t an external reference receiver. This setup is thought to enable vehicle attitude determination and RTK performance amelioration. In this thesis, we firstly proposed a method that includes an array of receivers with known geometry to enhance the performance of the RTK in different environments. Taking advantage of the attitude information and the known geometry of the installed array of receivers, the improvement of some internal steps of RTK w.r.t an external reference receiver can be achieved. The navigation module to be implemented in this work is an Extended Kalman Filter (EKF). The performance of a proposed two-receiver navigation architecture is then studied to quantify the improvements brought by the measurement redundancy. This concept is firstly tested on a simulator in order to validate the proposed algorithm and to give a reference result of our multi-receiver system’s performance. The pseudorange measurements and carrier phase measurements mathematical models are implemented in a realistic simulator. Different scenarios are conducted, including varying the distance between the 2 antennas of the receiver array, the satellite constellation geometry, and the amplitude of the noise measurement, in order to determine the influence of the use of an array of receivers. The simulation results show that our multi-receiver RTK system w.r.t an external reference receiver is more robust to noise and degraded satellite geometry, in terms of ambiguity fixing rate, and gets a better position accuracy under the same conditions when compared with the single receiver system. Additionally, our method achieves a relatively accurate estimation of the attitude of the vehicle which provides additional information beyond the positioning. In order to optimize our processing, the correlation of the measurement errors affecting observations taken by our array of receivers has been determined. Then, the performance of our real-time single frequency cycle-slip detection and repair algorithm has been assessed. These two investigations yielded important information so as to tune our Kalman Filter. The results obtained from the simulation made us eager to use actual data to verify and improve our multi-receiver RTK and attitude system. Tests based on real data collected around Toulouse, France, are used to test the performance of the whole methodology, where different scenarios are conducted, including varying the distance between the 2 antennas of the receiver array as well as the environmental conditions (open sky, suburban, and constrained urban environments). The thesis also tried to take advantage of a dual GNSS constellation, GPS and Galileo, to further strengthen the position solution and the reliable use of carrier phase measurements. The results show that our multi-receiver RTK system is more robust to degraded GNSS environments. Our experiments correlate favorably with our previous simulation results and further support the idea of using an array of receivers with known geometry to improve the RTK performance

Trustworthy precise point positioning with global navigation satellite systems

With the modernization of the Global Navigation Satellite System (GNSS), GNSS precise point positioning (PPP) technology becomes popular benefiting from its wide coverage and high accuracy. However, PPP technology still has many challenges in terms of continuity, fast convergence, and integrity monitoring, and these unsolved issues result in limitations of engineering applications. In this thesis, a reliable PPP technology with GNSS is investigated. The main contributions of the thesis are as follows: (1) A new cycle slip repair method that uses multiple epochs of time-differencing and geometry-based observations are proposed which has a significant improvement in the success rate of cycle slip repairs compared to existing methods. The positioning results also reflect that this method can reduce position errors and improve the continuity of PPP technology. (2) A systematic comparison of current interpolation methods used for high-accuracy regional ionospheric corrections is presented. It is found that each method has essentially the same accuracy in a small regional network with only a few stations, while the Kriging interpolation method can significantly improve the accuracy when the size of the network increases. Besides, a new method for predicting the uncertainty after broadcasting by grid point is also proposed. It has been validated that it is significantly closer to reality than other existing methods. In addition, different ionospheric correction implementation methods at the user end are also compared. (3) A integrity monitoring scheme for use in PPP based on real-time kinematic (RTK) positioning networks (PPP-RTK) with regional atmospheric corrections has been developed, which is based on the impacts of faults on the estimators considering possible faults in undifferenced and uncombined measurements. (4) Procedures for integrity monitoring considering the risks caused by incorrect ambiguity fixing are investigated. Two different methods for considering the probability of wrong ambiguity fixing including categorizing it into unmonitored fault and categorizing it as an individual type of fault are proposed and analyzed. (5) An integrity monitoring (IM) scheme based on the single-epoch framework for PPP-RTK is also proposed in order to exclude the effects caused by using observations from multiple epochs. Different solutions and their related availability are evaluated based on the satellite geometry in the global area

BDS GNSS for Earth Observation

For millennia, human communities have wondered about the possibility of observing phenomena in their surroundings, and in particular those affecting the Earth on which they live. More generally, it can be conceptually defined as Earth observation (EO) and is the collection of information about the biological, chemical and physical systems of planet Earth. It can be undertaken through sensors in direct contact with the ground or airborne platforms (such as weather balloons and stations) or remote-sensing technologies. However, the definition of EO has only become significant in the last 50 years, since it has been possible to send artificial satellites out of Earth’s orbit. Referring strictly to civil applications, satellites of this type were initially designed to provide satellite images; later, their purpose expanded to include the study of information on land characteristics, growing vegetation, crops, and environmental pollution. The data collected are used for several purposes, including the identification of natural resources and the production of accurate cartography. Satellite observations can cover the land, the atmosphere, and the oceans. Remote-sensing satellites may be equipped with passive instrumentation such as infrared or cameras for imaging the visible or active instrumentation such as radar. Generally, such satellites are non-geostationary satellites, i.e., they move at a certain speed along orbits inclined with respect to the Earth’s equatorial plane, often in polar orbit, at low or medium altitude, Low Earth Orbit (LEO) and Medium Earth Orbit (MEO), thus covering the entire Earth’s surface in a certain scan time (properly called ’temporal resolution’), i.e., in a certain number of orbits around the Earth. The first remote-sensing satellites were the American NASA/USGS Landsat Program; subsequently, the European: ENVISAT (ENVironmental SATellite), ERS (European Remote-Sensing satellite), RapidEye, the French SPOT (Satellite Pour l’Observation de laTerre), and the Canadian RADARSAT satellites were launched. The IKONOS, QuickBird, and GeoEye-1 satellites were dedicated to cartography. The WorldView-1 and WorldView-2 satellites and the COSMO-SkyMed system are more recent. The latest generation are the low payloads called Small Satellites, e.g., the Chinese BuFeng-1 and Fengyun-3 series. Also, Global Navigation Satellite Systems (GNSSs) have captured the attention of researchers worldwide for a multitude of Earth monitoring and exploration applications. On the other hand, over the past 40 years, GNSSs have become an essential part of many human activities. As is widely noted, there are currently four fully operational GNSSs; two of these were developed for military purposes (American NAVstar GPS and Russian GLONASS), whilst two others were developed for civil purposes such as the Chinese BeiDou satellite navigation system (BDS) and the European Galileo. In addition, many other regional GNSSs, such as the South Korean Regional Positioning System (KPS), the Japanese quasi-zenital satellite system (QZSS), and the Indian Regional Navigation Satellite System (IRNSS/NavIC), will become available in the next few years, which will have enormous potential for scientific applications and geomatics professionals. In addition to their traditional role of providing global positioning, navigation, and timing (PNT) information, GNSS navigation signals are now being used in new and innovative ways. Across the globe, new fields of scientific study are opening up to examine how signals can provide information about the characteristics of the atmosphere and even the surfaces from which they are reflected before being collected by a receiver. EO researchers monitor global environmental systems using in situ and remote monitoring tools. Their findings provide tools to support decision makers in various areas of interest, from security to the natural environment. GNSS signals are considered an important new source of information because they are a free, real-time, and globally available resource for the EO community

Kinematic Precise Point Positioning Algorithm Development and Improvements using External Atmospheric Information

Precise point positioning (PPP) is a high-accuracy GNSS positioning technique used to process single-receiver, dual-frequency carrier-phase and pseudorange measurements using precise network-estimated satellite clock and orbit data products, along with optional satellite carrier phase bias and attitude information. The PPP strategy does not require any nearby reference stations and has therefore gained interest in many commercial and scientific industries over the past few decades. However, kinematic PPP can be affected by large positioning errors in the presence of ionospheric scintillation, under strong ionospheric gradients, and during strong tropospheric storm events. Therefore, this thesis aims to develop new methods that incorporate additional external atmospheric information into the positioning model to improve kinematic PPP accuracy under these harsh atmospheric conditions. Ionospheric scintillation of GNSS signals is caused by plasma density irregularities in the ionosphere and is characterized by rapid phase and amplitude fluctuations of the received signal. In equatorial regions, between ±20° geomagnetic latitude, strong and frequent post-sunset scintillation is common and can amplify positioning errors by several orders of magnitude. However, an increased number of satellites using modernized signals could help to mitigate this impact. Therefore, this thesis evaluates kinematic PPP performance using multi-GNSS processing under low latitude ionospheric scintillation conditions. Compared to GPS-only processing, multi-GNSS configurations using Galileo measurements achieved respective average vertical positioning accuracy and precision improvements equal to 3.4-cm (39.8%) and 1.8-cm (52.7%). In addition, multi-GNSS configurations improved daily respective horizontal and vertical position accuracy and precision by up to 13.0-cm (80.4%) and 13.6-cm (90.4%) during the worst GPS-only processing day. Although multi-GNSS processing can improve kinematic PPP performance under ionospheric scintillation conditions, a non-mitigated satellite elevation-based stochastic model degrades positioning accuracy when high-elevation satellites are affected by moderate or strong scintillation. Furthermore, scintillation mitigation using receiver tracking error outputs in a modified stochastic model is affected by frequent outages under strong scintillation conditions and has only been demonstrated for single-system processing. Therefore, this thesis develops repaired and normalized multi-GNSS receiver tracking error model outputs to respectively increase mitigation availability and expand mitigation benefits to non-specialized users that may require a mixed stochastic approach. The proposed techniques were evaluated using GPS+Galileo measurement processing for a common geodetic receiver under moderate and strong low latitude ionospheric scintillation conditions. Relative to a standard elevation-based stochastic model, the mitigated approach improved the daily worst-case 3D kinematic PPP error by 16.6-cm (46.7%) and 13.6-cm (37.4%) for the two best cases, while the average 3D position error for both stochastic methods agreed at the cm-level in all cases. Tropospheric effects are typically addressed in GNSS processing by a priori hydrostatic correction models and estimation of zenith wet delay and horizontal gradient components. However, rapid changes in atmospheric water vapor caused by heavy rainfall can amplify tropospheric asymmetry effects and reduce kinematic PPP accuracy due to tightly constrained tropospheric parameters. Therefore, this thesis develops and evaluates deterministic, partially stochastic, and fully stochastic correction methods that use progressively more GNSS network-estimated tropospheric data under extreme tropospheric storm conditions to improve the achievable kinematic PPP accuracy at user locations. Comparison with the non-corrected model revealed that the fully stochastic approach improved the hourly horizontal and vertical position error by up to 3.2-cm (45.5%) and 10.2-cm (66.2%), respectively, while deterministic and partially stochastic methods improved only the vertical positioning error component. Increased ionospheric activity for high-elevation satellites can amplify otherwise stable positioning errors in an elevation-based stochastic model unless the stochastic model is modified with user-estimated ionospheric delay information to amplify measurement noise. However, this technique relies on continuous dual-frequency carrier phase measurements that are assumed to be free from cycle slip effects, which is not guaranteed in challenging ionospheric environments due to measurement outages and poor-quality carrier phase data. Therefore, this thesis suggests an alternative stochastic model strategy to amplify measurement noise using the rate of the ionospheric delay computed from external global and regional ionospheric map products that are independent of cycle slips and outages that a GNSS user may experience. For low latitude stations evaluated relative to a standard satellite elevation-based stochastic model, the proposed technique successfully improved maximum 3D kinematic PPP error by up to 15.6-cm (52.5%) when the global ionospheric map product was used. Extreme variability of the experimental 60-second update rate regional ionospheric map data deactivated the modified stochastic approach for 88.8% of epochs which resulted in positioning performance identical to the elevation-based method at the mm-level

Kinematic Precise Point Positioning Algorithm Development and Improvements using External Atmospheric Information

Precise point positioning (PPP) is a high-accuracy GNSS positioning technique used to process single-receiver, dual-frequency carrier-phase and pseudorange measurements using precise network-estimated satellite clock and orbit data products, along with optional satellite carrier phase bias and attitude information. The PPP strategy does not require any nearby reference stations and has therefore gained interest in many commercial and scientific industries over the past few decades. However, kinematic PPP can be affected by large positioning errors in the presence of ionospheric scintillation, under strong ionospheric gradients, and during strong tropospheric storm events. Therefore, this thesis aims to develop new methods that incorporate additional external atmospheric information into the positioning model to improve kinematic PPP accuracy under these harsh atmospheric conditions. Ionospheric scintillation of GNSS signals is caused by plasma density irregularities in the ionosphere and is characterized by rapid phase and amplitude fluctuations of the received signal. In equatorial regions, between ±20° geomagnetic latitude, strong and frequent post-sunset scintillation is common and can amplify positioning errors by several orders of magnitude. However, an increased number of satellites using modernized signals could help to mitigate this impact. Therefore, this thesis evaluates kinematic PPP performance using multi-GNSS processing under low latitude ionospheric scintillation conditions. Compared to GPS-only processing, multi-GNSS configurations using Galileo measurements achieved respective average vertical positioning accuracy and precision improvements equal to 3.4-cm (39.8%) and 1.8-cm (52.7%). In addition, multi-GNSS configurations improved daily respective horizontal and vertical position accuracy and precision by up to 13.0-cm (80.4%) and 13.6-cm (90.4%) during the worst GPS-only processing day. Although multi-GNSS processing can improve kinematic PPP performance under ionospheric scintillation conditions, a non-mitigated satellite elevation-based stochastic model degrades positioning accuracy when high-elevation satellites are affected by moderate or strong scintillation. Furthermore, scintillation mitigation using receiver tracking error outputs in a modified stochastic model is affected by frequent outages under strong scintillation conditions and has only been demonstrated for single-system processing. Therefore, this thesis develops repaired and normalized multi-GNSS receiver tracking error model outputs to respectively increase mitigation availability and expand mitigation benefits to non-specialized users that may require a mixed stochastic approach. The proposed techniques were evaluated using GPS+Galileo measurement processing for a common geodetic receiver under moderate and strong low latitude ionospheric scintillation conditions. Relative to a standard elevation-based stochastic model, the mitigated approach improved the daily worst-case 3D kinematic PPP error by 16.6-cm (46.7%) and 13.6-cm (37.4%) for the two best cases, while the average 3D position error for both stochastic methods agreed at the cm-level in all cases. Tropospheric effects are typically addressed in GNSS processing by a priori hydrostatic correction models and estimation of zenith wet delay and horizontal gradient components. However, rapid changes in atmospheric water vapor caused by heavy rainfall can amplify tropospheric asymmetry effects and reduce kinematic PPP accuracy due to tightly constrained tropospheric parameters. Therefore, this thesis develops and evaluates deterministic, partially stochastic, and fully stochastic correction methods that use progressively more GNSS network-estimated tropospheric data under extreme tropospheric storm conditions to improve the achievable kinematic PPP accuracy at user locations. Comparison with the non-corrected model revealed that the fully stochastic approach improved the hourly horizontal and vertical position error by up to 3.2-cm (45.5%) and 10.2-cm (66.2%), respectively, while deterministic and partially stochastic methods improved only the vertical positioning error component. Increased ionospheric activity for high-elevation satellites can amplify otherwise stable positioning errors in an elevation-based stochastic model unless the stochastic model is modified with user-estimated ionospheric delay information to amplify measurement noise. However, this technique relies on continuous dual-frequency carrier phase measurements that are assumed to be free from cycle slip effects, which is not guaranteed in challenging ionospheric environments due to measurement outages and poor-quality carrier phase data. Therefore, this thesis suggests an alternative stochastic model strategy to amplify measurement noise using the rate of the ionospheric delay computed from external global and regional ionospheric map products that are independent of cycle slips and outages that a GNSS user may experience. For low latitude stations evaluated relative to a standard satellite elevation-based stochastic model, the proposed technique successfully improved maximum 3D kinematic PPP error by up to 15.6-cm (52.5%) when the global ionospheric map product was used. Extreme variability of the experimental 60-second update rate regional ionospheric map data deactivated the modified stochastic approach for 88.8% of epochs which resulted in positioning performance identical to the elevation-based method at the mm-level

Zur GNSS-basierten Bestimmung von Position und Geschwindigkeit in der Fluggravimetrie

Das weltumspannende Satelliten-Navigationssystem GNSS spielt eine wichtige Rolle für die Fluggravimetrie. Gegenstand dieser Arbeit ist die Entwicklung zuverlässiger GNSS-Algorithmen und Software für die hochgenaue GNSS-Datenanalyse in der Fluggravimetrie. Ausgehend von den Anforderungen für praktische Anwendungen der Fluggravimetrie lassen sich die Beiträge und Schwerpunkte dieser Dissertation wie folgt zusammenfassen: Ausgleichs- bzw. Schätzungs-Algorithmen: Ausgehend von den Genauigkeitsanforderungen an die GNSS-basierte Positionsbestimmung in der Fluggravimetrie werden in einer kinematischen GNSS-Daten-Auswertung eine Schätzung nach kleinsten Quadraten einschließlich der Eliminierung von Störparametern sowie ein Zwei-Wege-Kalman-Filter angewendet. Das Ziel der beiden Ausgleichsverfahren ist es, an jedem Messzeitpunkt zunächst globale Parameter (wie System-Fehler und Trägerwellen-Ambiguities) und anschließend lokale Parameter (wie Position und Geschwindigkeit der bewegten Messplattform) zu bestimmen. Die angewandten Methoden sind sehr effizient und ergeben hochpräzise Resultate für die GNSS-Datenanalyse. Analyse von Genauigkeit und Zuverlässigkeit: Die Genauigkeit und Zuverlässigkeit der Resultate der präzisen kinematischen GNSS-Positionsbestimmung werden untersucht. Dabei wird eine besondere Methode zur Bewertung der Genauigkeit der kinematischen GNSS-Positionsbestimmung vorgeschlagen, wo bekannte Entfernungen zwischen mehreren GNSS-Antennen als Genauigkeits-Maßstab genommen werden. Weiterhin wird der Einfluss der Uhrenfehler der GNSS-Empfänger auf die Genauigkeit der kinematischen Positionsbestimmung für die Hochgeschwindigkeits-Plattform untersucht. Für dabei auftretende Probleme wird eine Lösung vorgeschlagen. Algorithmen der kinematischen Positionsbestimmung die auf mehreren Referenzstationen beruhen: Um das Problem der im Falle langer Basislinien abnehmenden Genauigkeit in der relativen kinematischen GNSS-Positionsbestimmung zu bewältigen, wird ein neuer Algorithmus vorgeschlagen. Er beruht auf der apriori Einführung von Exzentrizitäts-Bedingungen für mehrere Referenzstationen. Dieser Algorithmus erhöht die Genauigkeit und Zuverlässigkeit der Ergebnise in der kinematischen Positionsbestimmung für große Regionen resp. lange Basislinien. Präzise GNSS-Positionsbestimmung, beruhend auf robuster Schätzung: Das Vorhandensein von groben Fehlern in den GNSS-Beobachtungen verursacht das Auftreten von Ausreißern in den Ergebnissen der Positionsbestimmung. Um dieses Problem zu überwinden, wird ein robuster Ausgleichungs-Algorithmus angewendet, der die Auswirkungen von gro-ben Fehlern in den Ergebnissen der kinematischen GNSS-Positionsbestimmung beseitigt. Kinematische Positionierung auf der Basis mehrerer bewegter Stationen: In der Fluggravimetrie werden in der Regel mehrere GNSS-Antennen auf einer bewegten Plattform installiert. In diesem Zusammenhang wird deshalb erstens ein kinematisches GNSS-Positionsbestimmungsverfahren vorgeschlagen, das auf mehreren gleichzeitig bewegten GNSS-Stationen basiert. Aus den bekannten, konstanten Distanzen zwischen den GNSS-Antennen werden dabei apriori Exzentrizitäts-Bedingungen abgeleitet und in die Positions-schätzung eingeführt. Dies verbessert die Zuverlässigkeit des Messsystems. Zweitens wird solch ein Ansatz auch zur Bestimmung eines gemeinsamen Refraktionsparameters aller GNSS-Antennen der Plattform für den feuchten Teil der Atmosphäre verwendet. Dieses Verfahren reduziert nicht nur die Menge der geschätzten Parameter, sondern verringert auch die Korrelation zwischen den atmosphärischen Parametern. Kinematische Positionierung basierend auf der Kombination verschiedener GNSS-Systeme: Um die Zuverlässigkeit und Genauigkeit der kinematischen Positionsbestimmung zu verbessern, werden die Signale mehrerer GNSS-Systeme (d.h. GPS und GLONASS) gemeinsam registriert und ausgewertet (sog. GNSS-Integration). Zur Optimierung des relativen Gewichts zwischen den Daten der verschiedenen GNSS-Systeme wird die Helmertsche Varianz-Komponenten-Schätzung angewandt. Der auf dieser Basis entwickelte Kombinationsalgorithmus ermöglicht die Verbesserung der Beiträge von mehreren GNSS-Systemen. Geschwindigkeitsbestimmung mit GNSS-Doppler-Daten: Die Auswertung der Schwere-Messdaten in der Fluggravimetrie verlangt die hochgenaue Bestimmung des Geschwindigkeitsvektors der bewegten Plattform. Deshalb werden rohe GNSS-Doppler-Beobachtungen verwendet, um die Geschwindigkeit der bewegten Plattform im Falle hoch-dynamischer Flugbedingungen kinematisch zu bestimmen. Darüberhinaus werden aus der Trägerphase abgeleitete Doppler-Beobachtungen verwendet, um präzise Geschwindigkeitsschätzungen im Falle weniger dynamischer Flugbedingungen zu erhalten. Die Kombination verschiedener GNSS-Systeme wird auch bei der Doppler-Geschwindigkeitsbestimmung angewandt. Hierzu wird die Anwendung der Helmertschen Varianzkomponenten-Schätzung und einer robusten Schätzung untersucht. Software Entwicklung und Anwendung: Um die aktuellen Anforderungen der GNSS-basierten Positionsbestimmung in der Flug- sowie Schiffsgravimetrie zu erfüllen, wurde ein Software-System (HALO_GNSS) für die präzise kinematische GNSS-Flugbahn- und Geschwindigkeitsberechnung kinematischer Plattformen entwickelt. Die in dieser Arbeit vorgeschlagenen Algorithmen wurden in diese Software integriert. Um die Effizienz der vorgeschlagenen Algorithmen und der HALO_GNSS Software zu prüfen, wurde diese Software sowohl in Flug- als auch in Schiffsgravimetrie-Projekten des GFZ Potsdam angewandt. Alle Ergebnisse werden verglichen und geprüft und es wird gezeigt, dass die angewandten Methoden die Zuverlässigkeit und Genauigkeit der kinematischen Positions- und Geschwindigkeitsbestimmung effektiv verbessern. Die Verwendung der Software HA-LO_GNSS ermöglicht kinematische Positionsbestimmung mit einer Genauigkeit von 1-2 cm sowie Geschwindigkeitsbestimmung mit einer Genauigkeit von ca. 1 cm/s mit Roh- und etwa 1 mm/s mit aus der Trägerphase abgeleiteten Doppler-Beobachtungen.The Global Navigation Satellite System (GNSS) plays a significant role in the fields of airborne gravimetry. The objective of this thesis is to develop reliable GNSS algorithms and software for kinematic highly precise GNSS data analysis in airborne gravimetry. Based on the requirements for practical applications in airborne gravimetry and shipborne gravimetry projects, the core research and the contributions of this thesis are summarized as follows: Estimation Algorithm: Based on the accuracy requirements for GNSS precise positioning in airborne gravimetry, the estimation algorithms of least squares including the elimination of nuisance parameters as well as a two-way Kalman filter are applied to the kinematic GNSS data post-processing. The goal of these adjustment methods is to calculate non-epoch parameters (such as system error estimates or carrier phase ambiguity parameters) using all data in the first step, followed by the calculation of epoch parameters (such as position and velocity parameters of the kinematic platform) at every epoch. These methods are highly efficient when dealing with massive amounts of data, and give the highly precise results for the GNSS data analyzed. Accuracy Evaluation and Reliability Analysis: The accuracy evaluation and reliability analysis of the results from precise kinematic GNSS positioning is studied. A special accuracy evaluation method in GNSS kinematic positioning is proposed, where the known distances among multiple antennas of GNSS receivers are taken as an accuracy evaluation index. The effect of the GNSS receiver clock error in the accuracy evaluation for GNSS kinematic positioning results of a high-speed motion platform is studied and a solution is proposed. Kinematic Positioning Based on Multiple Reference Stations Algorithms: In order to overcome the problem of decreasing accuracy in GNSS relative kinematic positioning for long baselines, a new relative kinematic positioning method based on a priori constraints for multiple reference stations is proposed. This algorithm increases the accuracy and reliability of kinematic positioning results for large regions resp. long baselines. GNSS Precise Positioning Based on Robust Estimation: In order to solve the problem of outliers occurring in positioning results which are caused by the presence of gross errors in the GNSS observations, a robust estimation algorithm is applied to eliminate the effects of gross errors in the results of GNSS kinematic precise positioning. Kinematic Positioning Based on Multiple Kinematic Stations: In airborne gravimetry, multiple antennas of GNSS receivers are usually mounted on the kinematic platform. Firstly, a GNSS kinematic positioning method based on multiple kinematic stations is proposed. Using the known constant distances among the multiple GNSS antennas, a kinematic positioning method based on a priori distance constraints is proposed to improve the reliability of the system. Secondly, such an approach is also used for the estimation of a common atmospheric wet delay parameter among the multiple GNSS antennas mounted on the platform. This method does not only reduce the amount of estimated parameters, but also decreases the correlation among the atmospheric parameters. Kinematic Positioning Based on GNSS Integration: To improve the reliability and accuracy of kinematic positioning, a kinematic positioning method using multiple GNSS systems integration is addressed. Furthermore, a GNSS integration algorithm based on Helmert’s variance components estimation is proposed to adjust the weights in a reasonable way. This improves the results when combining data of the different GNSS systems. Velocity Determination Using GNSS Doppler Data: Airborne gravimetry requires instantaneous velocity results, thus raw Doppler observations are used to determine the kinematic instantaneous velocity in high-dynamic environments. Furthermore, carrier phase derived Doppler observations are used to obtain precise velocity estimates in low-dynamic environments. Then a method of Doppler velocity determination based on GNSS integration with Helmert’s variance components estimation and robust estimation is studied. Software Development and Application: In order to fulfill the actual requirements of airborne as well as shipborne gravimetry on GNSS precise positioning, a software system (HALO_GNSS) for precise kinematic GNSS trajectory and velocity determination for kinematic platforms has been developed. In this software, the algorithms as proposed in this thesis were adopted and applied. In order to evaluate the effectiveness of the proposed algorithm and the HALO_GNSS software, this software is applied in airborne as well as shipborne gravimetry projects of GFZ Potsdam. All results are compared and examined, and it is shown that the applied approaches can effectively improve the reliability and accuracy of the kinematic position and velocity determination. It allows the kinematic positioning with an accuracy of 1-2 cm and the velocity determination with an accuracy of approximately 1 cm/s using raw and approximately 1 mm/s using carrier phase derived Doppler observations