This thesis investigates autonomous satellite navigation using gravity gradiometry as an alternative to GNSS. While GNSS depends on external signals, gravity gradiometry allows a satellite to determine its orbit using only on-board measurements and gravity field models. A least squares orbit determination algorithm is developed in Rust, and tested with both uncorrected and corrected versions. The corrected version accounts for calibration parameters such as bias, while the uncorrected does not.

Analyses show that an accurate convergence depends on initial position deviations, orbital altitude, degree and order, calibration parameters and dampening factor. Validation with ESA’s GOCE mission data demonstrates average position errors of 0.688%, primarily due to measurement errors. Applying the algorithm to simulated lunar orbit data yields errors of 0.0000981658%, a significant improvement under more ideal conditions.

The study concludes that gravity gradiometry offers a viable path toward autonomous navigation, with future work needed on calibration refinements and broader orbital testing.

This thesis investigates the feasibility of the usage of space-based Laser Ablation Propulsion (LAP) to perform orbit maintenance on CubeSat in Low Earth Orbit (LEO). The thesis is motivated by the growing need for efficient, compact propulsion systems for small satellites and explores LAP as a method that eliminates the need for onboard fuel by generating momentum via directed laser beams from orbital laser stations. A detailed simulation framework is developed to model the physical environment in LEO, including atmospheric drag, solar radiation pressure, and third-body gravitational effects. The propagation of high-powered lasers through space is modeled considering diffraction, absorption, and current limits of aiming precision. The propulsion interaction between laser and CubeSat is simulated through a dynamic model based on material properties and laser beam parameters. Power generation and energy storage are analyzed in the context of space-based constraints, comparing solar arrays, battery technologies, and their degradation over time. A financial model evaluates system-level trade-offs, including station cost, maintenance intervals, and replacement economics. Optimizations are performed to understand how propulsion efficiency, energy storage capacity, and environmental variability affect system performance and scalability. Simulation results show that LAP is viable under current technological constraints, provided stations are deployed in sufficient numbers and at the right altitudes. The system can service a meaningful number of CubeSats while remaining competitive with the cost of satellite replacement. Technological limitations such as aiming accuracy and beam control are identified as key challenges, with recommendations for future work including the expansion of in-orbit LAP to networks, and further applications.

The Swarm satellite mission, in operation since November of 2013, has been collecting high-accuracy GPS tracking data for over a decade. Kinematic orbit solutions derived from this data have been used as input to gravity field estimation techniques for some time, with research initiatives such as the 'Multi-approach gravity field models from Swarm GPS data' dedicated to the production of highly accurate time-varying models. The production of these models is of great value to the geodetic community, as they provide an alternative and independent source of gravity field information which can be used to bridge gaps in the service of dedicated gravity field missions such as the Gravity Recovery and Climate Experiment, and provide additional temporal and spatial coverage of Earth's gravity field. The determination of this time-varying component is of great scientific value, as these models capture the complex motion of mass about the Earth occurring over short periods of time; such as the melting of polar icecaps and drought. The focus of this thesis is the improvement of the Swarm kinematic orbits used as input for such models.

We begin with an introduction to the research, including background of the topic, the scope of the research, the current state of the art and the gap in knowledge to be addressed culminating in the presentation of research questions. Input kinematic orbits from the Technical University of Delft, the Astronomical Institute of the University of Bern and the Institute of Geodesy Graz are retrieved. We apply input consolidation using interpolation to bring each input to consistent 1-second frequency sampling. We then apply a 0.3m outlier-filtering using the residuals of each input with respect to a reduced dynamic reference orbit. The consolidated and pre-screened input orbits are then combined using the following techniques: arithmetic mean weighting, inverse-variance weighting, iterative variance component estimation, weighting with the residuals with respect to a \ac{RDO} reference, and optimisation using an \ac{RDO} reference.

The resulting combined orbits are then assessed using \ac{SLR} residuals retrieved using the 'GNSS High Precision Orbit Determination Software Tools' of the German Aerospace Center. An additional analysis of the uncertainty of these orbits is performed using the projection of the orbit uncertainty along the line-of-sight vector from the satellite laser ranging station to the satellite. Results demonstrate that the combined orbit solutions outperform the input orbits across a range of metrics, with the best-performing combined orbits produced using the arithmetic mean method achieving an \ac{SLR} residual root mean-square error of 2 cm in 2022, and 4 cm in 2023, improving upon the next-best input orbit by 20 cm of error. This improvement is achieved without the loss of any volume of data. The combined orbits are additionally less sensitive to poor tracking data, and are therefore more robust than the inputs. These combined orbits show promise as use in gravity field estimation from Swarm kinematic orbits, as an improvement in the input orbits corresponds to an improvement in the gravity field model.

Finally, we explore the advantage of using SLR residuals to re-scale the input orbit noise such that each input is statistically consistent. We find that this technique further improves the quality of the combined orbits created using the inverse variance and variance component estimation techniques.

This study investigates the calibration of accelerometer data for the Next Generation Gravity Mission (NGGM), proposed by the European Space Agency. With improved precision, NGGM aims to continue gravity field observations beyond the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission. The mission consists of a satellite pair measuring Earth's gravity field using an onboard laser tracking instrument. To isolate the gravity field signature in these observations, each satellite carries accelerometers to estimate non-gravitational accelerations. This thesis supports the accelerometer calibration process by applying lessons from previous gravity field missions.

A historical review of gravity missions highlights the evolution of scientific and hardware requirements. The study examines accelerometer principles, sources of instrumental imperfections, and existing data calibration techniques. NGGM’s preliminary design includes multiple accelerometers placed away from the satellite’s center of mass, allowing the use of shaking manoeuvres—first introduced in the GOCE mission—for calibration.

A comprehensive model is developed that can generate shaking manoeuvres with varying thrust magnitudes, shaking durations, and shaking frequencies to excite the satellite. This model is used in conjunction with various accelerometer units (two, three, and four accelerometer layouts are considered) and their placement in the satellite's body frame to evaluate the calibration quality against the scientific requirements posed for the mission.

Results indicate that along-track accelerometer placement minimizes non-gravitational acceleration measurement errors due to enhanced centrifugal acceleration from the satellite’s pitch rate during calibration. Furthermore, the along-track placement performs better than radial placement, even though it has the same centrifugal acceleration boost. The suspected cause is the electrode layout of the accelerometer, which boosts the acceleration signal due to the projection of the angular acceleration about the y-axis onto the z component of the linear acceleration. The radial placement of the accelerometers provides no additional signal to the x component of the linear acceleration due to a lack of projection. Lower shaking frequencies improve calibration by accumulating higher angular rates over time. However, due to volume constraints imposed by the laser tracking instrument, cross-track placement may be more favourable. This configuration requires higher thrust levels, as the absence of a pitch rate signal on the cross-track axis worsens the signal-to-noise ratio of the observations, which warrants a revision of the thruster requirements and accelerometer performance. Moreover, more than two accelerometers reduce measurement errors by providing redundancy in the observations. Even with three accelerometers placed in the along-track direction, at least 24 hours of shaking at maximum thrust, as stated by the thruster requirement, is required for effective calibration. Lower thrust or shorter shaking durations would necessitate four accelerometers—two on the x-axis and two on the y-axis. Finally, the accelerometer pair’s arm length is treated as a free variable, as it has minimal impact on calibration performance.

This report provides foundational insight for future gravity missions. Smart accelerometer placement and shaking manoeuvre parameters can improve the measurement quality of the non-gravitational forces and subsequently improve gravity field recovery, which is crucial for tackling the climate crisis.

Since its launch in 2002, NASA’s dual-satellite GRACE mission has provided invaluable data on Earth’s gravity field, enabling the study of mass redistribution through changes in equivalent water height (EWH). However, gaps in GRACE data hinder accurate climate modelling. This thesis investigates gap-filling using neural networks, with a focus on error and uncertainty quantification. Neural networks are trained on ESA’s Swarm EWH data and NASA’s GLDAS soil moisture data to predict GRACE EWH. The impact of sampling additional training data using quantified input errors is also explored. An experiment involving 20,400 models across major river basins demonstrates that neural networks achieve lower errors in EWH gravity field solutions compared to Swarm. The inclusion of additional training data significantly reduces errors and uncertainties, underscoring the potential of incorporating noise-level variation to enhance future models and improve climate change predictions.

Recent years have seen an increased need for a lunar navigation service to support the large volume of missions planned for the coming decade. Supplying lunar navigation from the Earth suffers from limitations deriving from the long distance to the Moon. Thus, it must rely on a constellation of lunar navigation satellites. This thesis addresses the Orbit Determination (OD) of the satellites of a Lunar Navigation System (LNS) using range and Doppler observations from terrestrial stations. It provides a recommendation for the values of the design parameters of the OD system of a LNS to achieve the required OD accuracy and evaluates the impact of non-design factors such as the constellation's geometry and the observational noise. The parameters of the orbit propagation and the length of the estimation arc are identified as drivers of the OD accuracy. The relation between OD errors and navigation performance is also assessed.