This thesis addresses the issue of space debris tracking through the development and evaluation of advanced radar-based Initial Orbit Determination (IOD) algorithms and optimised measurement strategies. Space debris presents a challenge to space missions, necessitating tracking methods that do not require cooperation from the objects being tracked. The batch least squares estimator (BLSE), extended Kalman filter (EKF), unscented Kalman filter (UKF), and unscented batch estimator (UBE) were evaluated using both simulated and real radar data from the Sentinel-1A satellite. The UBE algorithm showed the lowest root-mean-square error (RMSE) and fastest convergence, effectively managing the non-linear complexities of orbit determination.
Four radar measurement strategies were examined to determine the minimum number of observations required for accurate orbit prediction and reacquisition. Strategies that provided full coverage and focused on the closest point of approach (CPA) demonstrated the most reliable results such as the inbound-CPA-outbound method and the changing radar update time. The study also investigated the impact of excluding specific radar measurements, such as range, radial velocity, azimuth, or elevation, on the accuracy of orbit estimation. It was found that a combination of range and angle measurements provided the most accurate results.