Hybrid Artificial-Intelligence-Based System for Unmanned Aerial Vehicle Detection, Localization, and Tracking Using Software-Defined Radio and Computer Vision Techniques

[EN] The proliferation of drones in civilian environments has raised growing concerns about their misuse, highlighting the need to develop efficient detection systems to protect public and private spaces. This article presents a hybrid approach for UAV detection that combines two artificial-intellig...

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Autores: López-Muñoz, Pablo, Abarca, Christian, Alegre, Francisco José, Calle, Jose Luis, Gimeno-San-Frutos, Luis, Monserrat del Río, Jose Francisco|||0000-0001-8664-6408
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/214752
Acceso en línea:https://riunet.upv.es/handle/10251/214752
Access Level:acceso abierto
Palabra clave:UAV
SDR
Autoencoders
YOLOv10
Tracking
Localization
TEORÍA DE LA SEÑAL Y COMUNICACIONES
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spelling Hybrid Artificial-Intelligence-Based System for Unmanned Aerial Vehicle Detection, Localization, and Tracking Using Software-Defined Radio and Computer Vision TechniquesLópez-Muñoz, PabloAbarca, ChristianAlegre, Francisco JoséCalle, Jose LuisGimeno-San-Frutos, LuisMonserrat del Río, Jose Francisco|||0000-0001-8664-6408UAVSDRAutoencodersYOLOv10TrackingLocalizationTEORÍA DE LA SEÑAL Y COMUNICACIONES[EN] The proliferation of drones in civilian environments has raised growing concerns about their misuse, highlighting the need to develop efficient detection systems to protect public and private spaces. This article presents a hybrid approach for UAV detection that combines two artificial-intelligence-based methods to improve system accuracy. The first method uses a software-defined radio (SDR) to analyze the radio spectrum, employing autoencoders to detect drone control signals and identify the presence of these devices. The second method is a computer vision module consisting of fixed cameras and a PTZ camera, which uses the YOLOv10 object detection algorithm to identify UAVs in real time from video sequences. Additionally, this module integrates a localization and tracking algorithm, allowing the tracking of the intruding UAV's position. Experimental results demonstrate high detection accuracy, a significant reduction in false positives for both methods, and remarkable effectiveness in UAV localization and tracking with the PTZ camera. These findings position the proposed system as a promising solution for security applications.This research was funded by Red.Es.MDPI AGEscuela Técnica Superior de Ingeniería de TelecomunicaciónDepartamento de ComunicacionesInstituto Universitario de Telecomunicación y Aplicaciones MultimediaRepositorio Institucional de la Universitat Politècnica de València Riunet20242024-12-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/214752reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2147522026-06-13T07:49:27Z
dc.title.none.fl_str_mv Hybrid Artificial-Intelligence-Based System for Unmanned Aerial Vehicle Detection, Localization, and Tracking Using Software-Defined Radio and Computer Vision Techniques
title Hybrid Artificial-Intelligence-Based System for Unmanned Aerial Vehicle Detection, Localization, and Tracking Using Software-Defined Radio and Computer Vision Techniques
spellingShingle Hybrid Artificial-Intelligence-Based System for Unmanned Aerial Vehicle Detection, Localization, and Tracking Using Software-Defined Radio and Computer Vision Techniques
López-Muñoz, Pablo
UAV
SDR
Autoencoders
YOLOv10
Tracking
Localization
TEORÍA DE LA SEÑAL Y COMUNICACIONES
title_short Hybrid Artificial-Intelligence-Based System for Unmanned Aerial Vehicle Detection, Localization, and Tracking Using Software-Defined Radio and Computer Vision Techniques
title_full Hybrid Artificial-Intelligence-Based System for Unmanned Aerial Vehicle Detection, Localization, and Tracking Using Software-Defined Radio and Computer Vision Techniques
title_fullStr Hybrid Artificial-Intelligence-Based System for Unmanned Aerial Vehicle Detection, Localization, and Tracking Using Software-Defined Radio and Computer Vision Techniques
title_full_unstemmed Hybrid Artificial-Intelligence-Based System for Unmanned Aerial Vehicle Detection, Localization, and Tracking Using Software-Defined Radio and Computer Vision Techniques
title_sort Hybrid Artificial-Intelligence-Based System for Unmanned Aerial Vehicle Detection, Localization, and Tracking Using Software-Defined Radio and Computer Vision Techniques
dc.creator.none.fl_str_mv López-Muñoz, Pablo
Abarca, Christian
Alegre, Francisco José
Calle, Jose Luis
Gimeno-San-Frutos, Luis
Monserrat del Río, Jose Francisco|||0000-0001-8664-6408
author López-Muñoz, Pablo
author_facet López-Muñoz, Pablo
Abarca, Christian
Alegre, Francisco José
Calle, Jose Luis
Gimeno-San-Frutos, Luis
Monserrat del Río, Jose Francisco|||0000-0001-8664-6408
author_role author
author2 Abarca, Christian
Alegre, Francisco José
Calle, Jose Luis
Gimeno-San-Frutos, Luis
Monserrat del Río, Jose Francisco|||0000-0001-8664-6408
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Escuela Técnica Superior de Ingeniería de Telecomunicación
Departamento de Comunicaciones
Instituto Universitario de Telecomunicación y Aplicaciones Multimedia
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv UAV
SDR
Autoencoders
YOLOv10
Tracking
Localization
TEORÍA DE LA SEÑAL Y COMUNICACIONES
topic UAV
SDR
Autoencoders
YOLOv10
Tracking
Localization
TEORÍA DE LA SEÑAL Y COMUNICACIONES
description [EN] The proliferation of drones in civilian environments has raised growing concerns about their misuse, highlighting the need to develop efficient detection systems to protect public and private spaces. This article presents a hybrid approach for UAV detection that combines two artificial-intelligence-based methods to improve system accuracy. The first method uses a software-defined radio (SDR) to analyze the radio spectrum, employing autoencoders to detect drone control signals and identify the presence of these devices. The second method is a computer vision module consisting of fixed cameras and a PTZ camera, which uses the YOLOv10 object detection algorithm to identify UAVs in real time from video sequences. Additionally, this module integrates a localization and tracking algorithm, allowing the tracking of the intruding UAV's position. Experimental results demonstrate high detection accuracy, a significant reduction in false positives for both methods, and remarkable effectiveness in UAV localization and tracking with the PTZ camera. These findings position the proposed system as a promising solution for security applications.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-12-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/214752
url https://riunet.upv.es/handle/10251/214752
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869412848951623680
score 15,811543

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