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Improved Artificial Bee Colony Algorithm Based Gravity Matching Navigation Method

Gravity matching navigation algorithm is one of the key technologies for gravity aided inertial navigation systems. With the development of intelligent algorithms, the powerful search ability of the Artificial Bee Colony (ABC) algorithm makes it possible to be applied to the gravity matching navigat...

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Detalles Bibliográficos
Autores principales: Gao, Wei, Zhao, Bo, Zhou, Guang Tao, Wang, Qiu Ying, Yu, Chun Yang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4168447/
https://www.ncbi.nlm.nih.gov/pubmed/25046019
http://dx.doi.org/10.3390/s140712968
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author Gao, Wei
Zhao, Bo
Zhou, Guang Tao
Wang, Qiu Ying
Yu, Chun Yang
author_facet Gao, Wei
Zhao, Bo
Zhou, Guang Tao
Wang, Qiu Ying
Yu, Chun Yang
author_sort Gao, Wei
collection PubMed
description Gravity matching navigation algorithm is one of the key technologies for gravity aided inertial navigation systems. With the development of intelligent algorithms, the powerful search ability of the Artificial Bee Colony (ABC) algorithm makes it possible to be applied to the gravity matching navigation field. However, existing search mechanisms of basic ABC algorithms cannot meet the need for high accuracy in gravity aided navigation. Firstly, proper modifications are proposed to improve the performance of the basic ABC algorithm. Secondly, a new search mechanism is presented in this paper which is based on an improved ABC algorithm using external speed information. At last, modified Hausdorff distance is introduced to screen the possible matching results. Both simulations and ocean experiments verify the feasibility of the method, and results show that the matching rate of the method is high enough to obtain a precise matching position.
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spelling pubmed-41684472014-09-19 Improved Artificial Bee Colony Algorithm Based Gravity Matching Navigation Method Gao, Wei Zhao, Bo Zhou, Guang Tao Wang, Qiu Ying Yu, Chun Yang Sensors (Basel) Article Gravity matching navigation algorithm is one of the key technologies for gravity aided inertial navigation systems. With the development of intelligent algorithms, the powerful search ability of the Artificial Bee Colony (ABC) algorithm makes it possible to be applied to the gravity matching navigation field. However, existing search mechanisms of basic ABC algorithms cannot meet the need for high accuracy in gravity aided navigation. Firstly, proper modifications are proposed to improve the performance of the basic ABC algorithm. Secondly, a new search mechanism is presented in this paper which is based on an improved ABC algorithm using external speed information. At last, modified Hausdorff distance is introduced to screen the possible matching results. Both simulations and ocean experiments verify the feasibility of the method, and results show that the matching rate of the method is high enough to obtain a precise matching position. MDPI 2014-07-18 /pmc/articles/PMC4168447/ /pubmed/25046019 http://dx.doi.org/10.3390/s140712968 Text en © 2014 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 license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Gao, Wei
Zhao, Bo
Zhou, Guang Tao
Wang, Qiu Ying
Yu, Chun Yang
Improved Artificial Bee Colony Algorithm Based Gravity Matching Navigation Method
title Improved Artificial Bee Colony Algorithm Based Gravity Matching Navigation Method
title_full Improved Artificial Bee Colony Algorithm Based Gravity Matching Navigation Method
title_fullStr Improved Artificial Bee Colony Algorithm Based Gravity Matching Navigation Method
title_full_unstemmed Improved Artificial Bee Colony Algorithm Based Gravity Matching Navigation Method
title_short Improved Artificial Bee Colony Algorithm Based Gravity Matching Navigation Method
title_sort improved artificial bee colony algorithm based gravity matching navigation method
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4168447/
https://www.ncbi.nlm.nih.gov/pubmed/25046019
http://dx.doi.org/10.3390/s140712968
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