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Distributed Space Debris Tracking with Consensus Labeled Random Finite Set Filtering †
Space debris tracking is a challenge for spacecraft operation because of the increasing number of both satellites and the amount of space debris. This paper investigates space debris tracking using marginalized [Formula: see text]-generalized labeled multi-Bernoulli filtering on a network of nodes c...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6165170/ https://www.ncbi.nlm.nih.gov/pubmed/30205536 http://dx.doi.org/10.3390/s18093005 |
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author | Wei, Baishen Nener, Brett |
author_facet | Wei, Baishen Nener, Brett |
author_sort | Wei, Baishen |
collection | PubMed |
description | Space debris tracking is a challenge for spacecraft operation because of the increasing number of both satellites and the amount of space debris. This paper investigates space debris tracking using marginalized [Formula: see text]-generalized labeled multi-Bernoulli filtering on a network of nodes consisting of a collection of sensors with different observation volumes. A consensus algorithm is used to achieve the global average by iterative regional averages. The sensor network can have unknown or time-varying topology. The proposed space debris tracking algorithm provides an efficient solution to the key challenges (e.g., detection uncertainty, data association uncertainty, clutter, etc.) for space situational awareness. The performance of the proposed algorithm is verified by simulation results. |
format | Online Article Text |
id | pubmed-6165170 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-61651702018-10-10 Distributed Space Debris Tracking with Consensus Labeled Random Finite Set Filtering † Wei, Baishen Nener, Brett Sensors (Basel) Article Space debris tracking is a challenge for spacecraft operation because of the increasing number of both satellites and the amount of space debris. This paper investigates space debris tracking using marginalized [Formula: see text]-generalized labeled multi-Bernoulli filtering on a network of nodes consisting of a collection of sensors with different observation volumes. A consensus algorithm is used to achieve the global average by iterative regional averages. The sensor network can have unknown or time-varying topology. The proposed space debris tracking algorithm provides an efficient solution to the key challenges (e.g., detection uncertainty, data association uncertainty, clutter, etc.) for space situational awareness. The performance of the proposed algorithm is verified by simulation results. MDPI 2018-09-07 /pmc/articles/PMC6165170/ /pubmed/30205536 http://dx.doi.org/10.3390/s18093005 Text en © 2018 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 Wei, Baishen Nener, Brett Distributed Space Debris Tracking with Consensus Labeled Random Finite Set Filtering † |
title | Distributed Space Debris Tracking with Consensus Labeled Random Finite Set Filtering † |
title_full | Distributed Space Debris Tracking with Consensus Labeled Random Finite Set Filtering † |
title_fullStr | Distributed Space Debris Tracking with Consensus Labeled Random Finite Set Filtering † |
title_full_unstemmed | Distributed Space Debris Tracking with Consensus Labeled Random Finite Set Filtering † |
title_short | Distributed Space Debris Tracking with Consensus Labeled Random Finite Set Filtering † |
title_sort | distributed space debris tracking with consensus labeled random finite set filtering † |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6165170/ https://www.ncbi.nlm.nih.gov/pubmed/30205536 http://dx.doi.org/10.3390/s18093005 |
work_keys_str_mv | AT weibaishen distributedspacedebristrackingwithconsensuslabeledrandomfinitesetfiltering AT nenerbrett distributedspacedebristrackingwithconsensuslabeledrandomfinitesetfiltering |