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Uncertainty Quantification for Space Situational Awareness and Traffic Management †

This paper presents a sensor-orientated approach to on-orbit position uncertainty generation and quantification for both ground-based and space-based surveillance applications. A mathematical framework based on the least squares formulation is developed to exploit real-time navigation measurements a...

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Detalles Bibliográficos
Autores principales: Hilton, Samuel, Cairola, Federico, Gardi, Alessandro, Sabatini, Roberto, Pongsakornsathien, Nichakorn, Ezer, Neta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6832602/
https://www.ncbi.nlm.nih.gov/pubmed/31600947
http://dx.doi.org/10.3390/s19204361
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author Hilton, Samuel
Cairola, Federico
Gardi, Alessandro
Sabatini, Roberto
Pongsakornsathien, Nichakorn
Ezer, Neta
author_facet Hilton, Samuel
Cairola, Federico
Gardi, Alessandro
Sabatini, Roberto
Pongsakornsathien, Nichakorn
Ezer, Neta
author_sort Hilton, Samuel
collection PubMed
description This paper presents a sensor-orientated approach to on-orbit position uncertainty generation and quantification for both ground-based and space-based surveillance applications. A mathematical framework based on the least squares formulation is developed to exploit real-time navigation measurements and tracking observables to provide a sound methodology that supports separation assurance and collision avoidance among Resident Space Objects (RSO). In line with the envisioned Space Situational Awareness (SSA) evolutions, the method aims to represent the navigation and tracking errors in the form of an uncertainty volume that accurately depicts the size, shape, and orientation. Simulation case studies are then conducted to verify under which sensors performance the method meets Gaussian assumptions, with a greater view to the implications that uncertainty has on the cyber-physical architecture evolutions and Cognitive Human-Machine Systems required for Space Situational Awareness and the development of a comprehensive Space Traffic Management framework.
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spelling pubmed-68326022019-11-25 Uncertainty Quantification for Space Situational Awareness and Traffic Management † Hilton, Samuel Cairola, Federico Gardi, Alessandro Sabatini, Roberto Pongsakornsathien, Nichakorn Ezer, Neta Sensors (Basel) Article This paper presents a sensor-orientated approach to on-orbit position uncertainty generation and quantification for both ground-based and space-based surveillance applications. A mathematical framework based on the least squares formulation is developed to exploit real-time navigation measurements and tracking observables to provide a sound methodology that supports separation assurance and collision avoidance among Resident Space Objects (RSO). In line with the envisioned Space Situational Awareness (SSA) evolutions, the method aims to represent the navigation and tracking errors in the form of an uncertainty volume that accurately depicts the size, shape, and orientation. Simulation case studies are then conducted to verify under which sensors performance the method meets Gaussian assumptions, with a greater view to the implications that uncertainty has on the cyber-physical architecture evolutions and Cognitive Human-Machine Systems required for Space Situational Awareness and the development of a comprehensive Space Traffic Management framework. MDPI 2019-10-09 /pmc/articles/PMC6832602/ /pubmed/31600947 http://dx.doi.org/10.3390/s19204361 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hilton, Samuel
Cairola, Federico
Gardi, Alessandro
Sabatini, Roberto
Pongsakornsathien, Nichakorn
Ezer, Neta
Uncertainty Quantification for Space Situational Awareness and Traffic Management †
title Uncertainty Quantification for Space Situational Awareness and Traffic Management †
title_full Uncertainty Quantification for Space Situational Awareness and Traffic Management †
title_fullStr Uncertainty Quantification for Space Situational Awareness and Traffic Management †
title_full_unstemmed Uncertainty Quantification for Space Situational Awareness and Traffic Management †
title_short Uncertainty Quantification for Space Situational Awareness and Traffic Management †
title_sort uncertainty quantification for space situational awareness and traffic management †
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6832602/
https://www.ncbi.nlm.nih.gov/pubmed/31600947
http://dx.doi.org/10.3390/s19204361
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