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Real-Time Multi-Target Localization from Unmanned Aerial Vehicles

In order to improve the reconnaissance efficiency of unmanned aerial vehicle (UAV) electro-optical stabilized imaging systems, a real-time multi-target localization scheme based on an UAV electro-optical stabilized imaging system is proposed. First, a target location model is studied. Then, the geod...

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Detalles Bibliográficos
Autores principales: Wang, Xuan, Liu, Jinghong, Zhou, Qianfei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5298606/
https://www.ncbi.nlm.nih.gov/pubmed/28029145
http://dx.doi.org/10.3390/s17010033
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author Wang, Xuan
Liu, Jinghong
Zhou, Qianfei
author_facet Wang, Xuan
Liu, Jinghong
Zhou, Qianfei
author_sort Wang, Xuan
collection PubMed
description In order to improve the reconnaissance efficiency of unmanned aerial vehicle (UAV) electro-optical stabilized imaging systems, a real-time multi-target localization scheme based on an UAV electro-optical stabilized imaging system is proposed. First, a target location model is studied. Then, the geodetic coordinates of multi-targets are calculated using the homogeneous coordinate transformation. On the basis of this, two methods which can improve the accuracy of the multi-target localization are proposed: (1) the real-time zoom lens distortion correction method; (2) a recursive least squares (RLS) filtering method based on UAV dead reckoning. The multi-target localization error model is established using Monte Carlo theory. In an actual flight, the UAV flight altitude is 1140 m. The multi-target localization results are within the range of allowable error. After we use a lens distortion correction method in a single image, the circular error probability (CEP) of the multi-target localization is reduced by 7%, and 50 targets can be located at the same time. The RLS algorithm can adaptively estimate the location data based on multiple images. Compared with multi-target localization based on a single image, CEP of the multi-target localization using RLS is reduced by 25%. The proposed method can be implemented on a small circuit board to operate in real time. This research is expected to significantly benefit small UAVs which need multi-target geo-location functions.
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spelling pubmed-52986062017-02-10 Real-Time Multi-Target Localization from Unmanned Aerial Vehicles Wang, Xuan Liu, Jinghong Zhou, Qianfei Sensors (Basel) Article In order to improve the reconnaissance efficiency of unmanned aerial vehicle (UAV) electro-optical stabilized imaging systems, a real-time multi-target localization scheme based on an UAV electro-optical stabilized imaging system is proposed. First, a target location model is studied. Then, the geodetic coordinates of multi-targets are calculated using the homogeneous coordinate transformation. On the basis of this, two methods which can improve the accuracy of the multi-target localization are proposed: (1) the real-time zoom lens distortion correction method; (2) a recursive least squares (RLS) filtering method based on UAV dead reckoning. The multi-target localization error model is established using Monte Carlo theory. In an actual flight, the UAV flight altitude is 1140 m. The multi-target localization results are within the range of allowable error. After we use a lens distortion correction method in a single image, the circular error probability (CEP) of the multi-target localization is reduced by 7%, and 50 targets can be located at the same time. The RLS algorithm can adaptively estimate the location data based on multiple images. Compared with multi-target localization based on a single image, CEP of the multi-target localization using RLS is reduced by 25%. The proposed method can be implemented on a small circuit board to operate in real time. This research is expected to significantly benefit small UAVs which need multi-target geo-location functions. MDPI 2016-12-25 /pmc/articles/PMC5298606/ /pubmed/28029145 http://dx.doi.org/10.3390/s17010033 Text en © 2016 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
Wang, Xuan
Liu, Jinghong
Zhou, Qianfei
Real-Time Multi-Target Localization from Unmanned Aerial Vehicles
title Real-Time Multi-Target Localization from Unmanned Aerial Vehicles
title_full Real-Time Multi-Target Localization from Unmanned Aerial Vehicles
title_fullStr Real-Time Multi-Target Localization from Unmanned Aerial Vehicles
title_full_unstemmed Real-Time Multi-Target Localization from Unmanned Aerial Vehicles
title_short Real-Time Multi-Target Localization from Unmanned Aerial Vehicles
title_sort real-time multi-target localization from unmanned aerial vehicles
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5298606/
https://www.ncbi.nlm.nih.gov/pubmed/28029145
http://dx.doi.org/10.3390/s17010033
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