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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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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. |
format | Online Article Text |
id | pubmed-7146742 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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