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A Novel Outdoor Positioning Technique Using LTE Network Fingerprints

In recent years, wireless-based fingerprint positioning has attracted increasing research attention owing to its position-related features and applications in the Internet of Things (IoT). In this paper, by leveraging long-term evolution (LTE) signals, a novel deep-learning-based fingerprint positio...

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Autores principales: Li, Da, Lei, Yingke, Zhang, Haichuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7146742/
https://www.ncbi.nlm.nih.gov/pubmed/32197380
http://dx.doi.org/10.3390/s20061691
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author Li, Da
Lei, Yingke
Zhang, Haichuan
author_facet Li, Da
Lei, Yingke
Zhang, Haichuan
author_sort Li, Da
collection PubMed
description In recent years, wireless-based fingerprint positioning has attracted increasing research attention owing to its position-related features and applications in the Internet of Things (IoT). In this paper, by leveraging long-term evolution (LTE) signals, a novel deep-learning-based fingerprint positioning approach is proposed to solve the problem of outdoor positioning. Considering the outstanding performance of deep learning in image classification, LTE signal measurements are converted into location grayscale images to form a fingerprint database. In order to deal with the instability of LTE signals, prevent the gradient dispersion problem, and increase the robustness of the proposed deep neural network (DNN), the following methods are adopted: First, cross-entropy is used as the loss function of the DNN. Second, the learning rate of the proposed DNN is dynamically adjusted. Third, this paper adopted several data enhancement techniques. To find the best positioning fingerprint and method, three types of fingerprint and five positioning models are compared. Finally, by using a deep residual network (Resnet) and transfer learning, a hierarchical structure training method is proposed. The proposed Resnet is used to train with the united fingerprint image database to obtain a positioning model called a coarse localizer. By using the prior knowledge of the pretrained Resnet, feed-forward neural network (FFNN)-based transfer learning is used to train with the united fingerprint database to obtain a better positioning model, called a fine localizer. The experimental results convincingly show that the proposed DNN can automatically learn the location features of LTE signals and achieve satisfactory positioning accuracy in outdoor environments.
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spelling pubmed-71467422020-04-20 A Novel Outdoor Positioning Technique Using LTE Network Fingerprints Li, Da Lei, Yingke Zhang, Haichuan Sensors (Basel) Article In recent years, wireless-based fingerprint positioning has attracted increasing research attention owing to its position-related features and applications in the Internet of Things (IoT). In this paper, by leveraging long-term evolution (LTE) signals, a novel deep-learning-based fingerprint positioning approach is proposed to solve the problem of outdoor positioning. Considering the outstanding performance of deep learning in image classification, LTE signal measurements are converted into location grayscale images to form a fingerprint database. In order to deal with the instability of LTE signals, prevent the gradient dispersion problem, and increase the robustness of the proposed deep neural network (DNN), the following methods are adopted: First, cross-entropy is used as the loss function of the DNN. Second, the learning rate of the proposed DNN is dynamically adjusted. Third, this paper adopted several data enhancement techniques. To find the best positioning fingerprint and method, three types of fingerprint and five positioning models are compared. Finally, by using a deep residual network (Resnet) and transfer learning, a hierarchical structure training method is proposed. The proposed Resnet is used to train with the united fingerprint image database to obtain a positioning model called a coarse localizer. By using the prior knowledge of the pretrained Resnet, feed-forward neural network (FFNN)-based transfer learning is used to train with the united fingerprint database to obtain a better positioning model, called a fine localizer. The experimental results convincingly show that the proposed DNN can automatically learn the location features of LTE signals and achieve satisfactory positioning accuracy in outdoor environments. MDPI 2020-03-18 /pmc/articles/PMC7146742/ /pubmed/32197380 http://dx.doi.org/10.3390/s20061691 Text en © 2020 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
Li, Da
Lei, Yingke
Zhang, Haichuan
A Novel Outdoor Positioning Technique Using LTE Network Fingerprints
title A Novel Outdoor Positioning Technique Using LTE Network Fingerprints
title_full A Novel Outdoor Positioning Technique Using LTE Network Fingerprints
title_fullStr A Novel Outdoor Positioning Technique Using LTE Network Fingerprints
title_full_unstemmed A Novel Outdoor Positioning Technique Using LTE Network Fingerprints
title_short A Novel Outdoor Positioning Technique Using LTE Network Fingerprints
title_sort novel outdoor positioning technique using lte network fingerprints
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7146742/
https://www.ncbi.nlm.nih.gov/pubmed/32197380
http://dx.doi.org/10.3390/s20061691
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