Autonomous 3-D UAV Localization Using Cellular Networks: Deep Supervised Learning Versus Reinforcement Learning Approaches

Unmanned aerial vehicles (UAVs) are becoming an integral part of numerous commercial and military applications. In many of these applications, the UAV is required to self-navigate in highly dynamic urban environments. This means that the UAV must have the ability to determine its location in an auto...

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Autores principales:	Ghada Afifi, Yasser Gadallah
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	5G UAV 3-D autonomous localization optimization deep learning reinforcement learning neural networks Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/fbb1e829b2864f3686de39f679eaf18b
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id	oai:doaj.org-article:fbb1e829b2864f3686de39f679eaf18b
record_format	dspace
spelling	oai:doaj.org-article:fbb1e829b2864f3686de39f679eaf18b2021-11-26T00:00:54ZAutonomous 3-D UAV Localization Using Cellular Networks: Deep Supervised Learning Versus Reinforcement Learning Approaches2169-353610.1109/ACCESS.2021.3126775https://doaj.org/article/fbb1e829b2864f3686de39f679eaf18b2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9606925/https://doaj.org/toc/2169-3536Unmanned aerial vehicles (UAVs) are becoming an integral part of numerous commercial and military applications. In many of these applications, the UAV is required to self-navigate in highly dynamic urban environments. This means that the UAV must have the ability to determine its location in an autonomous and real time manner. Existing localization techniques rely mainly on the Global Positioning System (GPS) and do not provide a reliable real time localization solution, particularly in dense urban environments. Our objective is to propose an effective alternative solution to enable the UAV to autonomously determine its location independent of the GPS and without message exchanges. We therefore propose utilizing the existing 5G cellular infrastructure to enable the UAV to determine its 3-D location without the need to interact with the cellular network. We formulate the UAV localization problem to minimize the error of the RSSI measurements from the surrounding cellular base stations. While exact optimization techniques can be applied to accurately solve such a problem, they cannot provide the real time calculation that is needed in such dynamic applications. Machine learning based techniques are strong candidates to provide an attractive alternative to provide a near-optimal localization solution with the needed practical real-time calculation. Accordingly, we propose two machine learning-based approaches, namely, deep neural network and reinforcement learning based approaches, to solve the formulated UAV localization problem in real time. We then provide a detailed comparative analysis for each of the proposed localization techniques along with a comparison with the optimization-based techniques as well as other techniques from the literature.Ghada AfifiYasser GadallahIEEEarticle5GUAV 3-D autonomous localizationoptimizationdeep learningreinforcement learningneural networksElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 155234-155248 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	5G UAV 3-D autonomous localization optimization deep learning reinforcement learning neural networks Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	5G UAV 3-D autonomous localization optimization deep learning reinforcement learning neural networks Electrical engineering. Electronics. Nuclear engineering TK1-9971 Ghada Afifi Yasser Gadallah Autonomous 3-D UAV Localization Using Cellular Networks: Deep Supervised Learning Versus Reinforcement Learning Approaches
description	Unmanned aerial vehicles (UAVs) are becoming an integral part of numerous commercial and military applications. In many of these applications, the UAV is required to self-navigate in highly dynamic urban environments. This means that the UAV must have the ability to determine its location in an autonomous and real time manner. Existing localization techniques rely mainly on the Global Positioning System (GPS) and do not provide a reliable real time localization solution, particularly in dense urban environments. Our objective is to propose an effective alternative solution to enable the UAV to autonomously determine its location independent of the GPS and without message exchanges. We therefore propose utilizing the existing 5G cellular infrastructure to enable the UAV to determine its 3-D location without the need to interact with the cellular network. We formulate the UAV localization problem to minimize the error of the RSSI measurements from the surrounding cellular base stations. While exact optimization techniques can be applied to accurately solve such a problem, they cannot provide the real time calculation that is needed in such dynamic applications. Machine learning based techniques are strong candidates to provide an attractive alternative to provide a near-optimal localization solution with the needed practical real-time calculation. Accordingly, we propose two machine learning-based approaches, namely, deep neural network and reinforcement learning based approaches, to solve the formulated UAV localization problem in real time. We then provide a detailed comparative analysis for each of the proposed localization techniques along with a comparison with the optimization-based techniques as well as other techniques from the literature.
format	article
author	Ghada Afifi Yasser Gadallah
author_facet	Ghada Afifi Yasser Gadallah
author_sort	Ghada Afifi
title	Autonomous 3-D UAV Localization Using Cellular Networks: Deep Supervised Learning Versus Reinforcement Learning Approaches
title_short	Autonomous 3-D UAV Localization Using Cellular Networks: Deep Supervised Learning Versus Reinforcement Learning Approaches
title_full	Autonomous 3-D UAV Localization Using Cellular Networks: Deep Supervised Learning Versus Reinforcement Learning Approaches
title_fullStr	Autonomous 3-D UAV Localization Using Cellular Networks: Deep Supervised Learning Versus Reinforcement Learning Approaches
title_full_unstemmed	Autonomous 3-D UAV Localization Using Cellular Networks: Deep Supervised Learning Versus Reinforcement Learning Approaches
title_sort	autonomous 3-d uav localization using cellular networks: deep supervised learning versus reinforcement learning approaches
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/fbb1e829b2864f3686de39f679eaf18b
work_keys_str_mv	AT ghadaafifi autonomous3duavlocalizationusingcellularnetworksdeepsupervisedlearningversusreinforcementlearningapproaches AT yassergadallah autonomous3duavlocalizationusingcellularnetworksdeepsupervisedlearningversusreinforcementlearningapproaches
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Autonomous 3-D UAV Localization Using Cellular Networks: Deep Supervised Learning Versus Reinforcement Learning Approaches

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