A precise spacecraft attitude dynamics model is essential for accurately predicting a satellite’s orientation in orbit,with such predictions being centerpiece to mission safety,operational control,and long-term risk ...A precise spacecraft attitude dynamics model is essential for accurately predicting a satellite’s orientation in orbit,with such predictions being centerpiece to mission safety,operational control,and long-term risk management.However,the highly nonlinear nature of spacecraft dynamics,compounded by uncertain and varying space perturbations such as atmospheric drag,solar radiation pressure,and magnetic torques,poses a substantial challenge to model fidelity.The problem is further exacerbated by the limited availability of in-orbit attitude measurements,which constrains direct calibration efforts.This work proposes a Bayesian stochastic model updating framework to systematically calibrate complex attitude dynamics models under epistemic and aleatory uncertainties.The methodology leverages approximate Bayesian computation with Euclidean and Bhattacharyya distance-based likelihoods,integrated within a transitional Markov chain Monte Carlo sampling scheme.A pseudo-online updating process is developed to incorporate sparse,sequential attitude measurements,enabling continual refinement of uncertain model parameters and improved characterization of stochastic dynamics.A numerical case study involving a rigid-body satellite subject to hybrid perturbations is presented to demonstrate the effectiveness of the proposed approach.The results show successful convergence of posterior distributions around true values,a significant reduction in epistemic uncertainty,and an improved predictive capability for attitude propagation in data-sparse scenarios.This framework offers a promising direction for enhancing attitude modeling reliability in the context of increasingly congested and observation-limited space environments.展开更多
文摘A precise spacecraft attitude dynamics model is essential for accurately predicting a satellite’s orientation in orbit,with such predictions being centerpiece to mission safety,operational control,and long-term risk management.However,the highly nonlinear nature of spacecraft dynamics,compounded by uncertain and varying space perturbations such as atmospheric drag,solar radiation pressure,and magnetic torques,poses a substantial challenge to model fidelity.The problem is further exacerbated by the limited availability of in-orbit attitude measurements,which constrains direct calibration efforts.This work proposes a Bayesian stochastic model updating framework to systematically calibrate complex attitude dynamics models under epistemic and aleatory uncertainties.The methodology leverages approximate Bayesian computation with Euclidean and Bhattacharyya distance-based likelihoods,integrated within a transitional Markov chain Monte Carlo sampling scheme.A pseudo-online updating process is developed to incorporate sparse,sequential attitude measurements,enabling continual refinement of uncertain model parameters and improved characterization of stochastic dynamics.A numerical case study involving a rigid-body satellite subject to hybrid perturbations is presented to demonstrate the effectiveness of the proposed approach.The results show successful convergence of posterior distributions around true values,a significant reduction in epistemic uncertainty,and an improved predictive capability for attitude propagation in data-sparse scenarios.This framework offers a promising direction for enhancing attitude modeling reliability in the context of increasingly congested and observation-limited space environments.
