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

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 F...

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Bibliographic Details
Published inAerospace science and technology Vol. 96; p. 105527
Main Authors Pesce, Vincenzo, Silvestrini, Stefano, Lavagna, Michèle
Format Journal Article
LanguageEnglish
Published Elsevier Masson SAS 01.01.2020
Online AccessGet full text
ISSN1270-9638
1626-3219
1626-3219
DOI10.1016/j.ast.2019.105527

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Summary: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.
ISSN:1270-9638
1626-3219
1626-3219
DOI:10.1016/j.ast.2019.105527