Mass property estimation, including mass, center of mass, and moment of inertia, is a crucial yet challenging problem in spacecraft autonomy and astrodynamics. Knowledge of mass properties of a spacecraft is essential for future astronautical missions, as changes in the mass properties of a spacecraft due to a shift in cargo distribution often require a careful and costly recalculation to ensure applied control inputs produce the desired results. As spacecraft missions grow in both duration and number, meeting the need for precise and accurate measurements becomes increasingly complex. Stochastic effects, such as angle and velocity random walks, along with persistent external disturbances, lead to drift in state measurements over time. These cumulative errors become significant during long duration missions. To mitigate these effects, some form of state estimation scheme becomes necessary. This thesis presents a robust adaptive estimation scheme, a dual UKF framework de- fined on the tangent bundle of the special Euclidean group, TSE(3), tailored explicitly for nonlinear mass property estimation. The external environmental disturbances due to massive primaries are then modeled via the circular restricted full three-body problem, which extends the classical circular restricted three-body problem by incorporating rigid-body dynamics through the SE(3) formulation. As process noise, a necessary statistical quantity of the system for Kalman-type filters, is often hard to analytically determine, the algorithm is made robust and adaptive to a variety of different systems through the use of a process noise estimation technique. In this thesis, two different methods of process noise estimation are compared to assess their performance and determine which may be an optimal approach. Furthermore, a dual method is selected for state and parameter estimation over a joint method for its robustness in the presence of noisy time-series data and for ease of implementation in the absence of a direct measurement model for mass properties. Finally, the numerical stability of the algorithm is investigated through Monte Carlo analysis, and its performance is demonstrated in numerical simulations of a rigid-body spacecraft in cislunar orbit.
","abstract_html":"<p>Mass property estimation, including mass, center of mass, and moment of inertia, is a crucial yet challenging problem in spacecraft autonomy and astrodynamics. Knowledge of mass properties of a spacecraft is essential for future astronautical missions, as changes in the mass properties of a spacecraft due to a shift in cargo distribution often require a careful and costly recalculation to ensure applied control inputs produce the desired results. As spacecraft missions grow in both duration and number, meeting the need for precise and accurate measurements becomes increasingly complex. Stochastic effects, such as angle and velocity random walks, along with persistent external disturbances, lead to drift in state measurements over time. These cumulative errors become significant during long duration missions. To mitigate these effects, some form of state estimation scheme becomes necessary. This thesis presents a robust adaptive estimation scheme, a dual UKF framework de- fined on the tangent bundle of the special Euclidean group, TSE(3), tailored explicitly for nonlinear mass property estimation. The external environmental disturbances due to massive primaries are then modeled via the circular restricted full three-body problem, which extends the classical circular restricted three-body problem by incorporating rigid-body dynamics through the SE(3) formulation. As process noise, a necessary statistical quantity of the system for Kalman-type filters, is often hard to analytically determine, the algorithm is made robust and adaptive to a variety of different systems through the use of a process noise estimation technique. In this thesis, two different methods of process noise estimation are compared to assess their performance and determine which may be an optimal approach. Furthermore, a dual method is selected for state and parameter estimation over a joint method for its robustness in the presence of noisy time-series data and for ease of implementation in the absence of a direct measurement model for mass properties. Finally, the numerical stability of the algorithm is investigated through Monte Carlo analysis, and its performance is demonstrated in numerical simulations of a rigid-body spacecraft in cislunar orbit.</p>","abstract_has_math":false,"creators":["Gunter, Herman"],"institution":null,"degree_name":"Master of Science in Aerospace Engineering","degree_level":"Thesis - Open Access","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-08-01T07:00:00Z","date_published":"2025-08-01T07:00:00Z","updated_at":"2026-07-27T19:26:16Z","subjects":["UKF","nonlinear filtering","adaptive filtering","robust filtering","state and parameter estimation","dual estimation","process noise estimation","on-manifold filtering","Astrodynamics","Navigation, Guidance, Control and Dynamics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/921","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Gunter, Herman"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Aerospace Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["UKF","nonlinear filtering","adaptive filtering","robust filtering","state and parameter estimation","dual estimation","process noise estimation","on-manifold filtering","Astrodynamics","Navigation, Guidance, Control and Dynamics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/921"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Mass property estimation, including mass, center of mass, and moment of inertia, is a crucial yet challenging problem in spacecraft autonomy and astrodynamics. Knowledge of mass properties of a spacecraft is essential for future astronautical missions, as changes in the mass properties of a spacecraft due to a shift in cargo distribution often require a careful and costly recalculation to ensure applied control inputs produce the desired results. As spacecraft missions grow in both duration and number, meeting the need for precise and accurate measurements becomes increasingly complex. Stochastic effects, such as angle and velocity random walks, along with persistent external disturbances, lead to drift in state measurements over time. These cumulative errors become significant during long duration missions. To mitigate these effects, some form of state estimation scheme becomes necessary. This thesis presents a robust adaptive estimation scheme, a dual UKF framework de- fined on the tangent bundle of the special Euclidean group, TSE(3), tailored explicitly for nonlinear mass property estimation. The external environmental disturbances due to massive primaries are then modeled via the circular restricted full three-body problem, which extends the classical circular restricted three-body problem by incorporating rigid-body dynamics through the SE(3) formulation. As process noise, a necessary statistical quantity of the system for Kalman-type filters, is often hard to analytically determine, the algorithm is made robust and adaptive to a variety of different systems through the use of a process noise estimation technique. In this thesis, two different methods of process noise estimation are compared to assess their performance and determine which may be an optimal approach. Furthermore, a dual method is selected for state and parameter estimation over a joint method for its robustness in the presence of noisy time-series data and for ease of implementation in the absence of a direct measurement model for mass properties. Finally, the numerical stability of the algorithm is investigated through Monte Carlo analysis, and its performance is demonstrated in numerical simulations of a rigid-body spacecraft in cislunar orbit.
"]},{"key":"dc:title","label":"Title","values":["Robust Adaptive Rigid Body State and Mass Property Estimation Via Unscented Kalman Filter on TSE(3) with Process Noise Estimation"]}]}],"canonical_facts":{"dc:creator":["Gunter, Herman"],"dc:description.abstract":["Mass property estimation, including mass, center of mass, and moment of inertia, is a crucial yet challenging problem in spacecraft autonomy and astrodynamics. Knowledge of mass properties of a spacecraft is essential for future astronautical missions, as changes in the mass properties of a spacecraft due to a shift in cargo distribution often require a careful and costly recalculation to ensure applied control inputs produce the desired results. As spacecraft missions grow in both duration and number, meeting the need for precise and accurate measurements becomes increasingly complex. Stochastic effects, such as angle and velocity random walks, along with persistent external disturbances, lead to drift in state measurements over time. These cumulative errors become significant during long duration missions. To mitigate these effects, some form of state estimation scheme becomes necessary. This thesis presents a robust adaptive estimation scheme, a dual UKF framework de- fined on the tangent bundle of the special Euclidean group, TSE(3), tailored explicitly for nonlinear mass property estimation. The external environmental disturbances due to massive primaries are then modeled via the circular restricted full three-body problem, which extends the classical circular restricted three-body problem by incorporating rigid-body dynamics through the SE(3) formulation. As process noise, a necessary statistical quantity of the system for Kalman-type filters, is often hard to analytically determine, the algorithm is made robust and adaptive to a variety of different systems through the use of a process noise estimation technique. In this thesis, two different methods of process noise estimation are compared to assess their performance and determine which may be an optimal approach. Furthermore, a dual method is selected for state and parameter estimation over a joint method for its robustness in the presence of noisy time-series data and for ease of implementation in the absence of a direct measurement model for mass properties. Finally, the numerical stability of the algorithm is investigated through Monte Carlo analysis, and its performance is demonstrated in numerical simulations of a rigid-body spacecraft in cislunar orbit.
"],"dc:identifier":["https://commons.erau.edu/edt/921"],"dc:subject":["UKF","nonlinear filtering","adaptive filtering","robust filtering","state and parameter estimation","dual estimation","process noise estimation","on-manifold filtering","Astrodynamics","Navigation, Guidance, Control and Dynamics"],"dc:title":["Robust Adaptive Rigid Body State and Mass Property Estimation Via Unscented Kalman Filter on TSE(3) with Process Noise Estimation"],"thesis:degree_discipline":["Aerospace Engineering"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science in Aerospace Engineering"]},"updated_at":"2026-07-27T19:26:16Z"}