Publication:
Covariance determination for improving uncertainty realism in orbit determination and propagation

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dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.contributor.authorCano Sanchez, Alejandro
dc.contributor.authorPastor Rodríguez, Alejandro
dc.contributor.authorEscobar, Diego
dc.contributor.authorMíguez Arenas, Joaquín
dc.contributor.authorSanjurjo Rivo, Manuel
dc.contributor.funderComunidad de Madrides
dc.date.accessioned2023-09-29T11:05:39Z
dc.date.available2023-09-29T11:05:39Z
dc.date.issued2023-10-01
dc.description.abstractThe reliability of the uncertainty characterization, also known as uncertainty realism, is of the uttermost importance for Space Situational Awareness (SSA) services. Among the many sources of uncertainty in the space environment, the most relevant one is the inherent uncertainty of the dynamic models, which is generally not considered in the batch least-squares Orbit Determination (OD) processes in operational scenarios. A classical approach to account for these sources of uncertainty is the theory of consider parameters. In this approach, a set of uncertain parameters are included in the underlying dynamical model, in such a way that the model uncertainty is represented by the variances of these parameters. However, realistic variances of these consider parameters are not known a priori. This work introduces a methodology to infer the variance of consider parameters based on the observed distribution of the Mahalanobis distance of the orbital differences between predicted and estimated orbits, which theoretically should follow a chi-square distribution under Gaussian assumptions. Empirical Distribution Function statistics such as the Cramer-von-Mises and the Kolmogorov–Smirnov distances are used to determine optimum consider parameter variances. The methodology is presented in this paper and validated in a series of simulated scenarios emulating the complexity of operational applications.en
dc.description.sponsorshipThis project has received funding from the "Comunidad de Madrid" under "Ayudas destinadas a la realización de doctorados industriales" program (project IND2020/TIC-17539).en
dc.format.extent19es
dc.identifier.bibliographicCitationCano, A., Pastor, A. P., Escobar, D. A., Míguez, J., & Sanjurjo-Rivo, M. (2023). Covariance determination for improving uncertainty realism in orbit determination and propagation. Advances in Space Research, 72(7), 2759-2777.en
dc.identifier.issn0273-1177
dc.identifier.publicationfirstpage2759es
dc.identifier.publicationissue7es
dc.identifier.publicationlastpage2777es
dc.identifier.publicationtitleAdvances in Space Researchen
dc.identifier.publicationvolume72es
dc.identifier.urihttps://hdl.handle.net/10016/38484
dc.identifier.uxxiAR/0000033400
dc.language.isoengen
dc.publisherElsevieren
dc.relation.projectIDComunidad de Madrid. IND2020/TIC-17539es
dc.relation.projectIDAT-2022es
dc.rights© 2022 COSPAR. Published by Elsevier B.V.en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaElectrónicaes
dc.subject.ecienciaInformáticaes
dc.subject.ecienciaIngeniería Industriales
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherUncertainty realismen
dc.subject.otherCovariance realismen
dc.subject.otherSpace situational awarenessen
dc.subject.otherCovariance determinationen
dc.subject.otherMahalanobis distanceen
dc.subject.otherChi-square distributionen
dc.subject.otherCramer-von-misesen
dc.subject.otherKolmogorov-smirnoven
dc.titleCovariance determination for improving uncertainty realism in orbit determination and propagationen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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