Available at: https://digitalcommons.calpoly.edu/theses/3168
Date of Award
9-2025
Degree Name
MS in Aerospace Engineering
Department/Program
Aerospace Engineering
College
College of Engineering
Advisor
Kira Abercromby
Advisor Department
Aerospace Engineering
Advisor College
College of Engineering
Abstract
The number of space objects (SOs) in low Earth orbit (LEO) continues to increase rapidly, creating challenges for the current ground-based tracking network, which cannot accommodate the projected growth in SOs. Catalog maintenance relies on frequent observations for reliable reacquisition, with Two-Line Element (TLE) sets typically generated daily to mitigate rapid error growth from poor TLE accuracy. This constraint limits the ability to track more objects with existing infrastructure. This work evaluates the Markov Chain Monte Carlo Ensemble Gaussian Mixture Filter (MCMC EnGMF), a nonlinear, non-Gaussian filter well-suited for sparse tracking scenarios where higher post-update accuracy is needed to reduce propagation error and enable longer gaps between observation arcs. The focus is on extending this gap to three days, enabling the current sensor network to track a greater number of SOs. Using precise ephemeris data from the Swarm constellation, the filter’s performance is assessed under sparse, angles-only measurement tracks. The MCMC EnGMF consistently achieves post-update position errors below 100 m, keeps pre-update angular errors within a 2 degree field of view, and remains robust to degraded initial conditions. With measurement noise of 4.41 arc-seconds, position errors drop below 7 m after three-day propagation, and further improvements are seen with longer arcs, additional tracks, and radar-based measurements. Additional test cases across varying orbital elements confirm the filter’s effectiveness for sparse, angles-only orbit estimation. These results demonstrate that the MCMC EnGMF enables accurate, sustained orbit estimation with significantly fewer observations, providing a viable approach to expanding tracking capacity in a congested LEO environment.