Space debris is becoming an ever increasing problem in space operations. Mitigation techniques exist, but require knowledge about debris objects in order to track them and predict where they will be in the future. One technique that is commonly used to estimate this information is called light curve inversion. The apparent brightness of an object passing overhead is measured from the ground, and information like orbit and attitude states can be estimated and object shapes can be characterized. This thesis focused on improving existing techniques for state estimation and shape characterisation for LEO objects. The state-of-the-art method, Multiple-Model Adaptive Estimation (MMAE), was implemented, tested and improved upon. Additionally, Variable-Structure Multiple-Model methods were implemented and tested. Testing involved simulating measurements for LEO satellites and running estimations. The improved MMAE algorithm was able to correctly identify the shapes of test objects for almost all test cases, with low attitude estimation errors.

This thesis investigates methods to enhance the robustness of space object cataloguing pipelines, focusing on tracklet correlation and orbit estimation using angular measurements from short observation arcs. The cataloguing robustness is defined as achieving high true positive and negative rates for tracklet correlation to allow for the build-up of an accurate object catalogue. The study addresses the main research question: How can the robustness of the cataloguing pipeline be improved when applying orbit estimation methods to the full angle set of short observation arcs?

A baseline tracklet correlation approach, based on the Boundary Value Problem (BVP) within the Admissible Region framework, is implemented. This method uses angular observations and hypothesized ranges to estimate an object's state, with correlations evaluated via a cost function based on the Mahalanobis distance. Classical IOD methods are employed to investigate their application toward validation of tracklet correlation when reconsidering the full angle set. The considered methods include the angles-only Gauss method, a multiple angles least-squares Gauss approach, Gooding’s method, as well as a Batch Least Squares (BLS) orbit determination (OD) method. The BVP and IOD methods consider two-body dynamics, and the BLS Earth's zonal harmonics and third body effects from the Sun and Moon. Simulated measurements are derived from Two Line Element sets (TLE) for initial reference states for LEO, MEO, and GEO objects, propagated with the SGP4 model accounting for Earth's atmospheric drag, zonal harmonics and third body Sun and Moon effects, providing the test data.

Results show that the BVP method performs best for GEO, achieving ~90% true positive rates with reasonable uncertainty gating. For LEO and MEO, higher thresholds and cost-function minima are required due to greater observation complexity and force-model discrepancy. Gooding’s method, making use of a Lambert solver, demonstrated robust performance across multiple orbital revolutions, while Gauss’ methods were less effective for large time gaps. Additionally, BLS struggled with sparse data and large time steps, offering limited state refinement despite higher computational expense.

The findings suggest gating based on chi-squared distribution thresholds for GEO and higher magnitudes for LEO and MEO to optimize true negative rates. While the BVP method provides sufficient accuracy for re-observation scenarios, classical IOD methods and BLS exhibit limitations under sparse tracklet conditions. This work highlights challenges in cataloguing lower-altitude objects, for ground-based optical observations, and suggests the application of the BVP method on lower altitudes requires inclusion of force models for the primary perturbations.

Cislunar space is getting increasingly important. Countries like the US and China are directing their attention towards the Moon with the Artemis and Chang’e missions. Simultaneously, space debris pollution of our orbits is escalating, increasing the risk of the Kessler syndrome occurring. Space Situational Awareness (SSA) aims to prevent this. The sheer amount of space debris makes continuous tracking unfeasible. This creates the need for accurate orbit determination and propagation between observations. Model frameworks have been developed extensively for space debris in near-Earth orbits, but there is little experience with cislunar space. This region is more challenging because of its unstable nature, increasing the risk of losing track of objects.

In this research, a model framework is developed, using the open-source Python package Tudat, that can estimate and propagate long-term cislunar space debris orbits accurately from optical data, whilst quantifying the uncertainties realistically over time using Monte Carlo simulation. Orbit determination has been performed using Weighted Least-Squares. The algorithm can estimate (amongst other parameters) initial states of the objects, model parameters like radiation pressure properties, and observation bias. We apply our framework to the Chang’e 2 and 3 upper stages. A selection of 13 estimation windows has allowed for analysis on a diverse range of orbital and observation characteristics. Moreover, the effect of close Moon approaches on orbit determination quality and propagation accuracy has been investigated.

A generic model framework has been found that achieves sufficient accuracy with reasonable computational load for all 13 use cases and can be used as a foundation for other cislunar space debris studies. The generic framework only estimates initial state, and uses a relatively simple dynamical model and integrator configuration. This allows sufficiently accurate propagation up to 2 years out-of-sample, depending strongly on the stability of the orbit. Afterwards, the generic model framework is tailored on individual use cases to improve its performance. Parameters like the radiation pressure coefficient and observation bias are estimated in this process. The tailored model framework improved performance for 8 out of 13 use cases (compared to the generic model framework), decreasing out-of-sample RMSE between 20-95% and increasing period of sufficient accuracy with up to 250 days. Estimating on 7-10 months of observations results in the best orbit determination quality and propagation accuracy for the cislunar use cases. The tailored model framework performs robustly for various non-linear orbits, except for out-of-sample close Moon approaches. Several solutions are proposed to solve this issue. Finally, it is found that the effect of uncertainty for cislunar space debris orbits over time is significant in the current framework. Uncertainty over time is especially large when estimating on short estimation windows (<4 months) and for orbits experiencing non-linear behaviour.