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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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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. |
format | Online Article Text |
id | pubmed-6832602 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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