Article (Scientific journals)
Bayesian inference of nongravitational perturbations from satellite observations
Dell'Elce, Lamberto; Gurfil, Pini; Ben-Yaacov, Ohad
2017In Journal of Guidance Control and Dynamics, 40 (5), p. 1231-1240
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Keywords :
Bayesian networks; Gravitation; Inference engines; Probability distributions; Satellites; Space debris; Uncertainty analysis; High-fidelity simulations; Joint probability distributions; Non-gravitational force; Nongravitational perturbations; Satellite observations; Solar radiation pressure; Space situational awareness; Third-body perturbations; Orbits
Abstract :
[en] Gravitational and third-body perturbations can be modeled with sufficient precision for most applications in low Earth orbit. However, owing to severe uncertainty sources and modeling limitations, computational models of satellite aerodynamics and solar radiation pressure are bound to be biased. Aiming at orbital propagation consistent with observed satellite orbital dynamics, real-time estimation of these perturbations is desired. In this paper, a particle filter for the recursive inference and prediction of nongravitational forces is developed. Specifically, after assuming a parametric model for the desired perturbations, the joint probability distribution of the parameters is inferred by using a prescribed number of weighted particles, each consisting of one set of orbital elements and one set of parameters. The particle evolution is carried out by means of an underlying orbital propagator, and the Bayes rule is used to recursively update weights by comparing propagated orbital elements with satellite observations. The proposed formulation uses mean orbital elements as the only available measurements. This feature makes the algorithm a potentially valuable resource for space situational awareness applications, such as space debris trajectories prediction from two-line elements, or for onboard force estimation from Global Positioning System data. High-fidelity simulations show that nongravitational perturbations can be estimated with 20% accuracy. © 2016 by the American Institute of Aeronautics and Astronautics, Inc.
Disciplines :
Aerospace & aeronautics engineering
Author, co-author :
Dell'Elce, Lamberto ;  Université de Liège - ULiège > Département d'aérospatiale et mécanique > Laboratoire de structures et systèmes spatiaux
Gurfil, Pini;  Distributed Space Systems Laboratory, Faculty of Aerospace Engineering, Technion-Israel Institute of Technology, Haifa, Israel
Ben-Yaacov, Ohad;  Distributed Space Systems Laboratory, Faculty of Aerospace Engineering, Technion-Israel Institute of Technology, Haifa, Israel
Language :
English
Title :
Bayesian inference of nongravitational perturbations from satellite observations
Publication date :
2017
Journal title :
Journal of Guidance Control and Dynamics
ISSN :
0731-5090
eISSN :
1533-3884
Publisher :
American Institute of Aeronautics and Astronautics Inc.
Volume :
40
Issue :
5
Pages :
1231-1240
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBi :
since 21 May 2020

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