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Two-UAV Intersection Localization System Based on the Airborne Optoelectronic Platform

To address the limitation of the existing UAV (unmanned aerial vehicles) photoelectric localization method used for moving objects, this paper proposes an improved two-UAV intersection localization system based on airborne optoelectronic platforms by using the crossed-angle localization method of ph...

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Detalles Bibliográficos
Autores principales: Bai, Guanbing, Liu, Jinghong, Song, Yueming, Zuo, Yujia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5298671/
https://www.ncbi.nlm.nih.gov/pubmed/28067814
http://dx.doi.org/10.3390/s17010098
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author Bai, Guanbing
Liu, Jinghong
Song, Yueming
Zuo, Yujia
author_facet Bai, Guanbing
Liu, Jinghong
Song, Yueming
Zuo, Yujia
author_sort Bai, Guanbing
collection PubMed
description To address the limitation of the existing UAV (unmanned aerial vehicles) photoelectric localization method used for moving objects, this paper proposes an improved two-UAV intersection localization system based on airborne optoelectronic platforms by using the crossed-angle localization method of photoelectric theodolites for reference. This paper introduces the makeup and operating principle of intersection localization system, creates auxiliary coordinate systems, transforms the LOS (line of sight, from the UAV to the target) vectors into homogeneous coordinates, and establishes a two-UAV intersection localization model. In this paper, the influence of the positional relationship between UAVs and the target on localization accuracy has been studied in detail to obtain an ideal measuring position and the optimal localization position where the optimal intersection angle is 72.6318°. The result shows that, given the optimal position, the localization root mean square error (RMS) will be 25.0235 m when the target is 5 km away from UAV baselines. Finally, the influence of modified adaptive Kalman filtering on localization results is analyzed, and an appropriate filtering model is established to reduce the localization RMS error to 15.7983 m. Finally, An outfield experiment was carried out and obtained the optimal results: [Formula: see text] , [Formula: see text] , [Formula: see text] , [Formula: see text] , where [Formula: see text] represents the longitude error, [Formula: see text] represents the latitude error, [Formula: see text] represents the altitude error, and [Formula: see text] represents the error radius.
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spelling pubmed-52986712017-02-10 Two-UAV Intersection Localization System Based on the Airborne Optoelectronic Platform Bai, Guanbing Liu, Jinghong Song, Yueming Zuo, Yujia Sensors (Basel) Article To address the limitation of the existing UAV (unmanned aerial vehicles) photoelectric localization method used for moving objects, this paper proposes an improved two-UAV intersection localization system based on airborne optoelectronic platforms by using the crossed-angle localization method of photoelectric theodolites for reference. This paper introduces the makeup and operating principle of intersection localization system, creates auxiliary coordinate systems, transforms the LOS (line of sight, from the UAV to the target) vectors into homogeneous coordinates, and establishes a two-UAV intersection localization model. In this paper, the influence of the positional relationship between UAVs and the target on localization accuracy has been studied in detail to obtain an ideal measuring position and the optimal localization position where the optimal intersection angle is 72.6318°. The result shows that, given the optimal position, the localization root mean square error (RMS) will be 25.0235 m when the target is 5 km away from UAV baselines. Finally, the influence of modified adaptive Kalman filtering on localization results is analyzed, and an appropriate filtering model is established to reduce the localization RMS error to 15.7983 m. Finally, An outfield experiment was carried out and obtained the optimal results: [Formula: see text] , [Formula: see text] , [Formula: see text] , [Formula: see text] , where [Formula: see text] represents the longitude error, [Formula: see text] represents the latitude error, [Formula: see text] represents the altitude error, and [Formula: see text] represents the error radius. MDPI 2017-01-06 /pmc/articles/PMC5298671/ /pubmed/28067814 http://dx.doi.org/10.3390/s17010098 Text en © 2017 by the authors; licensee MDPI, Basel, Switzerland. https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) ).
spellingShingle Article
Bai, Guanbing
Liu, Jinghong
Song, Yueming
Zuo, Yujia
Two-UAV Intersection Localization System Based on the Airborne Optoelectronic Platform
title Two-UAV Intersection Localization System Based on the Airborne Optoelectronic Platform
title_full Two-UAV Intersection Localization System Based on the Airborne Optoelectronic Platform
title_fullStr Two-UAV Intersection Localization System Based on the Airborne Optoelectronic Platform
title_full_unstemmed Two-UAV Intersection Localization System Based on the Airborne Optoelectronic Platform
title_short Two-UAV Intersection Localization System Based on the Airborne Optoelectronic Platform
title_sort two-uav intersection localization system based on the airborne optoelectronic platform
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5298671/
https://www.ncbi.nlm.nih.gov/pubmed/28067814
http://dx.doi.org/10.3390/s17010098
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AT zuoyujia twouavintersectionlocalizationsystembasedontheairborneoptoelectronicplatform