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Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks

Artificial Intelligence of things (AIoT) is the combination of Artificial Intelligence (AI) technologies and the Internet of Things (IoT) infrastructure. AI deals with the devices’ learning process to acquire knowledge from data and experience, while IoT concerns devices interacting with each other...

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Detalles Bibliográficos
Autores principales: Massimi, Federica, Ferrara, Pasquale, Benedetto, Francesco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824630/
https://www.ncbi.nlm.nih.gov/pubmed/36616722
http://dx.doi.org/10.3390/s23010124
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author Massimi, Federica
Ferrara, Pasquale
Benedetto, Francesco
author_facet Massimi, Federica
Ferrara, Pasquale
Benedetto, Francesco
author_sort Massimi, Federica
collection PubMed
description Artificial Intelligence of things (AIoT) is the combination of Artificial Intelligence (AI) technologies and the Internet of Things (IoT) infrastructure. AI deals with the devices’ learning process to acquire knowledge from data and experience, while IoT concerns devices interacting with each other using the Internet. AIoT has been proven to be a very effective paradigm for several existing applications as well as for new areas, especially in the field of satellite communication systems with mega-constellations. When AIoT meets space communications efficiently, we have interesting uses of AI for Satellite IoT (SIoT). In fact, the number of space debris is continuously increasing as well as the risk of space collisions, and this poses a significant threat to the sustainability and safety of space operations that must be carefully and efficiently addressed to avoid critical damage to the SIoT networks. This paper aims to provide a systematic survey of the state of the art, challenges, and perspectives on the use of deep learning methods for space situational awareness (SSA) object detection and classification. The contributions of this paper can be summarized as follows: (i) we outline using AI algorithms, and in particular, deep learning (DL) methods, the possibility of identifying the nature/type of spatial objects by processing signals from radars; (ii) we present a comprehensive taxonomy of DL-based methods applied to SSA object detection and classification, as well as their characteristics, and implementation issues.
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spelling pubmed-98246302023-01-08 Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks Massimi, Federica Ferrara, Pasquale Benedetto, Francesco Sensors (Basel) Review Artificial Intelligence of things (AIoT) is the combination of Artificial Intelligence (AI) technologies and the Internet of Things (IoT) infrastructure. AI deals with the devices’ learning process to acquire knowledge from data and experience, while IoT concerns devices interacting with each other using the Internet. AIoT has been proven to be a very effective paradigm for several existing applications as well as for new areas, especially in the field of satellite communication systems with mega-constellations. When AIoT meets space communications efficiently, we have interesting uses of AI for Satellite IoT (SIoT). In fact, the number of space debris is continuously increasing as well as the risk of space collisions, and this poses a significant threat to the sustainability and safety of space operations that must be carefully and efficiently addressed to avoid critical damage to the SIoT networks. This paper aims to provide a systematic survey of the state of the art, challenges, and perspectives on the use of deep learning methods for space situational awareness (SSA) object detection and classification. The contributions of this paper can be summarized as follows: (i) we outline using AI algorithms, and in particular, deep learning (DL) methods, the possibility of identifying the nature/type of spatial objects by processing signals from radars; (ii) we present a comprehensive taxonomy of DL-based methods applied to SSA object detection and classification, as well as their characteristics, and implementation issues. MDPI 2022-12-23 /pmc/articles/PMC9824630/ /pubmed/36616722 http://dx.doi.org/10.3390/s23010124 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Massimi, Federica
Ferrara, Pasquale
Benedetto, Francesco
Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks
title Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks
title_full Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks
title_fullStr Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks
title_full_unstemmed Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks
title_short Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks
title_sort deep learning methods for space situational awareness in mega-constellations satellite-based internet of things networks
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824630/
https://www.ncbi.nlm.nih.gov/pubmed/36616722
http://dx.doi.org/10.3390/s23010124
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