Improving Fingerprint Indoor Localization Using Convolutional Neural Networks
Two obstacles lie in the traditional Signal Strength Fingerprint Positioning method. Initially, the algorithm cannot converge quickly and accurately due to massive data generated by large indoor environment. Secondly, it is difficult to determine a specific floor in a building using the received Sig...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9237969/ |
| _version_ | 1819172999341801472 |
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| author | Danshi Sun Erhu Wei Li Yang Shiyi Xu |
| author_facet | Danshi Sun Erhu Wei Li Yang Shiyi Xu |
| author_sort | Danshi Sun |
| collection | DOAJ |
| description | Two obstacles lie in the traditional Signal Strength Fingerprint Positioning method. Initially, the algorithm cannot converge quickly and accurately due to massive data generated by large indoor environment. Secondly, it is difficult to determine a specific floor in a building using the received Signal Strength(RSS). This article proposes a method, which uses convolutional neural network (CNN) to classify the floor and location of Bluetooth RSS as well as magnetic field data to calculate the final coordinates, could apply Fingerprint Positioning into indoor environment with large areas and multiply floors. The method involves converting the collected Bluetooth RSS into the “fingerprint image” required for calculation and establishing the CNN for classification training. Subsequently, the real-time Bluetooth RSS are imported into the CNN to classify the floor and determine the transmitters' location. Additionally, the observer's coordinates are matched using the magnetic field data. Our experiments suggested that the proposed method can classify floors and transmitters' locations with predictable bunds of 0.9667 and 0.9333, respectively. At the same time, the average positioning error is less than 1.2 m, which is 43.32% and 44.67% higher than the traditional Bluetooth and magnetic field fingerprint positioning. The accuracy of dynamic positioning is also within 1.55 meters. |
| first_indexed | 2024-12-22T20:16:06Z |
| format | Article |
| id | doaj.art-2fc4a3edea224b9c8e18af5f2eacc160 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T20:16:06Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2fc4a3edea224b9c8e18af5f2eacc1602022-12-21T18:13:58ZengIEEEIEEE Access2169-35362020-01-01819339619341110.1109/ACCESS.2020.30333129237969Improving Fingerprint Indoor Localization Using Convolutional Neural NetworksDanshi Sun0https://orcid.org/0000-0001-8078-050XErhu Wei1Li Yang2Shiyi Xu3School of Geodesy and Geomatics, Wuhan University, Wuhan, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan, ChinaCollege of Environment and Planning, Henan University, Kaifeng, ChinaBeijing Satellite Navigation Center, Beijing, ChinaTwo obstacles lie in the traditional Signal Strength Fingerprint Positioning method. Initially, the algorithm cannot converge quickly and accurately due to massive data generated by large indoor environment. Secondly, it is difficult to determine a specific floor in a building using the received Signal Strength(RSS). This article proposes a method, which uses convolutional neural network (CNN) to classify the floor and location of Bluetooth RSS as well as magnetic field data to calculate the final coordinates, could apply Fingerprint Positioning into indoor environment with large areas and multiply floors. The method involves converting the collected Bluetooth RSS into the “fingerprint image” required for calculation and establishing the CNN for classification training. Subsequently, the real-time Bluetooth RSS are imported into the CNN to classify the floor and determine the transmitters' location. Additionally, the observer's coordinates are matched using the magnetic field data. Our experiments suggested that the proposed method can classify floors and transmitters' locations with predictable bunds of 0.9667 and 0.9333, respectively. At the same time, the average positioning error is less than 1.2 m, which is 43.32% and 44.67% higher than the traditional Bluetooth and magnetic field fingerprint positioning. The accuracy of dynamic positioning is also within 1.55 meters.https://ieeexplore.ieee.org/document/9237969/Fingerprint locationBluetoothmagnetic fieldconvolutional neural network (CNN)classification |
| spellingShingle | Danshi Sun Erhu Wei Li Yang Shiyi Xu Improving Fingerprint Indoor Localization Using Convolutional Neural Networks IEEE Access Fingerprint location Bluetooth magnetic field convolutional neural network (CNN) classification |
| title | Improving Fingerprint Indoor Localization Using Convolutional Neural Networks |
| title_full | Improving Fingerprint Indoor Localization Using Convolutional Neural Networks |
| title_fullStr | Improving Fingerprint Indoor Localization Using Convolutional Neural Networks |
| title_full_unstemmed | Improving Fingerprint Indoor Localization Using Convolutional Neural Networks |
| title_short | Improving Fingerprint Indoor Localization Using Convolutional Neural Networks |
| title_sort | improving fingerprint indoor localization using convolutional neural networks |
| topic | Fingerprint location Bluetooth magnetic field convolutional neural network (CNN) classification |
| url | https://ieeexplore.ieee.org/document/9237969/ |
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