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Cooperative Localization for Multi-AUVs Based on GM-PHD Filters and Information Entropy Theory

Cooperative localization (CL) is considered a promising method for underwater localization with respect to multiple autonomous underwater vehicles (multi-AUVs). In this paper, we proposed a CL algorithm based on information entropy theory and the probability hypothesis density (PHD) filter, aiming t...

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Detalles Bibliográficos
Autores principales: Zhang, Lichuan, Wang, Tonghao, Zhang, Feihu, Xu, Demin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5677013/
https://www.ncbi.nlm.nih.gov/pubmed/28991191
http://dx.doi.org/10.3390/s17102286
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author Zhang, Lichuan
Wang, Tonghao
Zhang, Feihu
Xu, Demin
author_facet Zhang, Lichuan
Wang, Tonghao
Zhang, Feihu
Xu, Demin
author_sort Zhang, Lichuan
collection PubMed
description Cooperative localization (CL) is considered a promising method for underwater localization with respect to multiple autonomous underwater vehicles (multi-AUVs). In this paper, we proposed a CL algorithm based on information entropy theory and the probability hypothesis density (PHD) filter, aiming to enhance the global localization accuracy of the follower. In the proposed framework, the follower carries lower cost navigation systems, whereas the leaders carry better ones. Meanwhile, the leaders acquire the followers’ observations, including both measurements and clutter. Then, the PHD filters are utilized on the leaders and the results are communicated to the followers. The followers then perform weighted summation based on all received messages and obtain a final positioning result. Based on the information entropy theory and the PHD filter, the follower is able to acquire a precise knowledge of its position.
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spelling pubmed-56770132017-11-17 Cooperative Localization for Multi-AUVs Based on GM-PHD Filters and Information Entropy Theory Zhang, Lichuan Wang, Tonghao Zhang, Feihu Xu, Demin Sensors (Basel) Article Cooperative localization (CL) is considered a promising method for underwater localization with respect to multiple autonomous underwater vehicles (multi-AUVs). In this paper, we proposed a CL algorithm based on information entropy theory and the probability hypothesis density (PHD) filter, aiming to enhance the global localization accuracy of the follower. In the proposed framework, the follower carries lower cost navigation systems, whereas the leaders carry better ones. Meanwhile, the leaders acquire the followers’ observations, including both measurements and clutter. Then, the PHD filters are utilized on the leaders and the results are communicated to the followers. The followers then perform weighted summation based on all received messages and obtain a final positioning result. Based on the information entropy theory and the PHD filter, the follower is able to acquire a precise knowledge of its position. MDPI 2017-10-08 /pmc/articles/PMC5677013/ /pubmed/28991191 http://dx.doi.org/10.3390/s17102286 Text en © 2017 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
Zhang, Lichuan
Wang, Tonghao
Zhang, Feihu
Xu, Demin
Cooperative Localization for Multi-AUVs Based on GM-PHD Filters and Information Entropy Theory
title Cooperative Localization for Multi-AUVs Based on GM-PHD Filters and Information Entropy Theory
title_full Cooperative Localization for Multi-AUVs Based on GM-PHD Filters and Information Entropy Theory
title_fullStr Cooperative Localization for Multi-AUVs Based on GM-PHD Filters and Information Entropy Theory
title_full_unstemmed Cooperative Localization for Multi-AUVs Based on GM-PHD Filters and Information Entropy Theory
title_short Cooperative Localization for Multi-AUVs Based on GM-PHD Filters and Information Entropy Theory
title_sort cooperative localization for multi-auvs based on gm-phd filters and information entropy theory
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5677013/
https://www.ncbi.nlm.nih.gov/pubmed/28991191
http://dx.doi.org/10.3390/s17102286
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