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Scalable Distributed State Estimation in UTM Context

This article proposes a novel approach to the Distributed State Estimation (DSE) problem for a set of co-operating UAVs equipped with heterogeneous on board sensors capable of exploiting certain characteristics typical of the UAS Traffic Management (UTM) context, such as high traffic density and the...

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Autores principales: Cicala, Marco, D’Amato, Egidio, Notaro, Immacolata, Mattei, Massimiliano
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7248766/
https://www.ncbi.nlm.nih.gov/pubmed/32397181
http://dx.doi.org/10.3390/s20092682
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author Cicala, Marco
D’Amato, Egidio
Notaro, Immacolata
Mattei, Massimiliano
author_facet Cicala, Marco
D’Amato, Egidio
Notaro, Immacolata
Mattei, Massimiliano
author_sort Cicala, Marco
collection PubMed
description This article proposes a novel approach to the Distributed State Estimation (DSE) problem for a set of co-operating UAVs equipped with heterogeneous on board sensors capable of exploiting certain characteristics typical of the UAS Traffic Management (UTM) context, such as high traffic density and the presence of limited range, Vehicle-to-Vehicle communication devices. The proposed algorithm is based on a scalable decentralized Kalman Filter derived from the Internodal Transformation Theory enhanced on the basis of the Consensus Theory. The general benefit of the proposed algorithm consists of, on the one hand, reducing the estimation problem to smaller local sub-problems, through a self-organization process of the local estimating nodes in response to the time varying communication topology; and on the other hand, of exploiting measures carried out nearby in order to improve the accuracy of the local estimates. In the UTM context, this enables each vehicle to estimate both its own position and velocity, as well as those of the neighboring vehicles, using both on board measurements and information transmitted by neighboring vehicles. A numerical simulation in a simplified UTM scenario is presented, in order to illustrate the salient aspects of the proposed algorithm.
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spelling pubmed-72487662020-08-13 Scalable Distributed State Estimation in UTM Context Cicala, Marco D’Amato, Egidio Notaro, Immacolata Mattei, Massimiliano Sensors (Basel) Article This article proposes a novel approach to the Distributed State Estimation (DSE) problem for a set of co-operating UAVs equipped with heterogeneous on board sensors capable of exploiting certain characteristics typical of the UAS Traffic Management (UTM) context, such as high traffic density and the presence of limited range, Vehicle-to-Vehicle communication devices. The proposed algorithm is based on a scalable decentralized Kalman Filter derived from the Internodal Transformation Theory enhanced on the basis of the Consensus Theory. The general benefit of the proposed algorithm consists of, on the one hand, reducing the estimation problem to smaller local sub-problems, through a self-organization process of the local estimating nodes in response to the time varying communication topology; and on the other hand, of exploiting measures carried out nearby in order to improve the accuracy of the local estimates. In the UTM context, this enables each vehicle to estimate both its own position and velocity, as well as those of the neighboring vehicles, using both on board measurements and information transmitted by neighboring vehicles. A numerical simulation in a simplified UTM scenario is presented, in order to illustrate the salient aspects of the proposed algorithm. MDPI 2020-05-08 /pmc/articles/PMC7248766/ /pubmed/32397181 http://dx.doi.org/10.3390/s20092682 Text en © 2020 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
Cicala, Marco
D’Amato, Egidio
Notaro, Immacolata
Mattei, Massimiliano
Scalable Distributed State Estimation in UTM Context
title Scalable Distributed State Estimation in UTM Context
title_full Scalable Distributed State Estimation in UTM Context
title_fullStr Scalable Distributed State Estimation in UTM Context
title_full_unstemmed Scalable Distributed State Estimation in UTM Context
title_short Scalable Distributed State Estimation in UTM Context
title_sort scalable distributed state estimation in utm context
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7248766/
https://www.ncbi.nlm.nih.gov/pubmed/32397181
http://dx.doi.org/10.3390/s20092682
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