Adaptive Neural Network-based Unscented Kalman Filter for Robust Pose Tracking of Noncooperative Spacecraft

This paper presents a neural network-based Unscented Kalman Filter (UKF) to estimate and track the pose (i.e., position and orientation) of a known, noncooperative, tumbling target spacecraft in a close-proximity rendezvous scenario. The UKF estimates the target's orbit and attitude relative to...

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Veröffentlicht in:	arXiv.org 2023-05
Hauptverfasser:	Tae Ha Park, D'Amico, Simone
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Attitudes Computer Science - Robotics Covariance matrix Domains Kalman filters Low earth orbits Neural networks Noise Rendezvous trajectories Robustness Space rendezvous Spacecraft Spacecraft tracking
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