Replacement of In-Orbit Extended Kalman Filter for Spacecraft Orbit Estimation via Neural Networks
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Spacecraft orbit estimation on-board a spacecraft could be utilized in order to minimize measurement and process noise effects. This could be done through an estimation algorithm such as Kalman Filter (KF) or Extended Kalman Filter (EKF). EKF utilizes an orbital motion model of the spacecraft. The model is characterized by high degree of complexity, nonlinearity, and coupling between system states. This complexity increases the computational load of the EKF and may represent a serious problem for real-time application over the spacecraft on-board computer. This is due to limited spacecraft on-board computer computational resources. In order to overcome this problem, an algorithm based on Neural Networks is developed. The required training data set has been obtained via EKF. Various disturbance forces and moments are considered, such as earth’s oblatenss effect till (J4), aerodynamic drag, solar radiation pressure, gravity gradient moment, and magnetic disturbance moments. The developed neural networks algorithm is applied to a verified test case spacecraft, which utilizes a GPS receiver in order to obtain spacecraft position measurements. The developed neural networks algorithm has the same accuracy of the EKF with an average execution time equal to 82% of EKF. This indicates better applicability for real-time application.
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