Attitude Estimation for Unresolved Agile Space Objects with Shape Model Uncertainty
Author(s)
Holzinger, Marcus J.
Alfriend, Kyle T.
Wetterer, Charles J
Luu, K. Kim
Sabol, Chris
Hamada, Kris
Harms, Andrew
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Abstract
The problem of estimating attitude for actively maneuvering or passively rotating Space Objects (SOs)
with unknown mass properties / external torques and uncertain shape models is addressed. To account
for agile SO maneuvers, angular rates are simply assumed to be random inputs (e.g., process noise), and
model uncertainty is accounted for in a bias state with dynamics derived using first principles. Bayesian
estimation approaches are used to estimate the resulting severely non-Gaussian and multi-modal state
distributions. Simulated results are given, conclusions regarding performance are made, and future work
is outlined.
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Date
2012-09
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Text
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Paper
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