Abstract
Presented within this work is a new method for inertial orbit estimation of an object, either known or unknown, adaptable to a network of low-cost observation satellites. The observation satellites would only require a monocular camera for line of sight measurements. Using the line of sight measurements of each observer, a pair of orthogonal geometric planes that intersect both the observation satellite and the target are created. The intersection of the two planes in the inertial frame defines the new measurement model that is implemented with multiple observation nodes. Total system observability is analyzed and the instantaneous (per node) observability is used to remove "bad" measurements from the system. The measurement model is used in an extended Kalman filter framework and the measurement noise nonlinear transformation is addressed. Three cases are presented; first, the minimum number of required observation nodes to produce accurate results if determined. Then, a smaller number of observation nodes is analyzed to highlight the use of the instantaneous observability and its deleterious effect on the filter performance. Finally, the method is expanded out to multiple observation satellites in a constellation. For all cases, the results show that this method is capable of producing accurate orbit estimation that converges in a short time.
Notes
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Graduation Date
2020
Semester
Spring
Advisor
Elgohary, Tarek
Degree
Master of Science in Aerospace Engineering (M.S.A.E.)
College
College of Engineering and Computer Science
Department
Mechanical and Aerospace Engineering
Degree Program
Aerospace Engineering; Space System Design and Engineering
Format
application/pdf
Identifier
CFE0008412; DP0023848
URL
https://purls.library.ucf.edu/go/DP0023848
Language
English
Release Date
November 2020
Length of Campus-only Access
None
Access Status
Masters Thesis (Open Access)
STARS Citation
Hippelheuser, James, "Inertial Orbit Estimation Using Multiple Space Based Observers: A New Measurement Model" (2020). Electronic Theses and Dissertations, 2020-2023. 440.
https://stars.library.ucf.edu/etd2020/440