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Radial basis function neural network aided adaptive extended Kalman filter for spacecraft relative navigation - 2019

Radial Basis Function Neural Network Aided Adaptive Extended Kalman Filter For Spacecraft Relative Navigation

Research Area:  Machine Learning

Abstract:

This paper presents a novel technique, combining neural network and Kalman filter, for state estimation. The proposed solution provides the estimates of the system states while also estimating the uncertain or unmodeled terms of the process dynamics. The developed algorithm exploits a Radial Basis Function Neural Network that outputs an estimate of the disturbances that are included in the prediction step of an Adaptive Extended Kalman Filter. A recursive form of adaptation is used to limit the computational burden. The proposed solution is compared to classical navigation filter implementations. A realistic spacecraft relative navigation scenario is selected to test the filter performance. Simulations are performed with accurate tuning and also in off-nominal conditions to test the filter robustness.

Keywords:  

Author(s) Name:  Vincenzo Pesce, Stefano Silvestrini, Michèle Lavagna

Journal name:  Aerospace Science and Technology

Conferrence name:  

Publisher name:  ELSEVIER

DOI:  https://doi.org/10.1016/j.ast.2019.105527

Volume Information:  Volume 96, January 2020, 105527