Several innovations are introduced for space object attitude estimation using light-curve measurements. A radiometric measurement noise model is developed to define the observation uncertainty in terms of optical, environmental, space object, and sensor parameters and is validated using experimental data. Additionally, a correlated process noise model is introduced to represent the angular acceleration dynamics. This model is used to account for the unknown inertia and body torques of agile space objects. This linear dynamics model enables the implementation of marginalized particle filters, affording computationally tractable three-degree-of-freedom Bayesian estimation. The synthesis of these novel approaches enables the estimation of attitude and angular velocity states of maneuvering space objects without a priori knowledge of initial attitude while maintaining computational tractability. Simulated results are presented for the full three-degree-of-freedom agile space object attitude estimation problem.
Bibliographical noteFunding Information:
This work was started at the Los Alamos Space Weather Summer School, funded by the Institute of Geophysics, Planetary Physics, and Signatures at Los Alamos National Laboratory (LANL) and continued while a U.S. Air Force Research Laboratory (AFRL) Directed Energy Scholar at the U.S. Air Force Maui Optical and Supercomputing (AMOS) site. The work was completed thanks to the support of the AFRL AMOS site and Integrity Applications Inc.– Pacific Defense Solutions (IAI-PDS) under contract FA6451-13-C-0281. IAI-PDS also provided the experimental Raven data included in this work. A special thanks is due to David Palmer of LANL, Kris Hamada of IAI-PDS, and Chris Sabol and Kim Luu of AFRL for their insightful and thought provoking feedback.
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