Indoor Positioning Based on Fingerprint-Image and Deep Learning
Wi-Fi and magnetic field fingerprinting have been a hot topic in indoor positioning researches because of their ubiquity and location-related features. Wi-Fi signals can provide rough initial positions, and magnetic fields can further improve the positioning accuracies, therefore many researchers ha...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8554268/ |
| _version_ | 1818644994872836096 |
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| author | Wenhua Shao Haiyong Luo Fang Zhao Yan Ma Zhongliang Zhao Antonino Crivello |
| author_facet | Wenhua Shao Haiyong Luo Fang Zhao Yan Ma Zhongliang Zhao Antonino Crivello |
| author_sort | Wenhua Shao |
| collection | DOAJ |
| description | Wi-Fi and magnetic field fingerprinting have been a hot topic in indoor positioning researches because of their ubiquity and location-related features. Wi-Fi signals can provide rough initial positions, and magnetic fields can further improve the positioning accuracies, therefore many researchers have tried to combine the two signals for high-accuracy indoor localization. Currently, state-of-the-art solutions design separate algorithms to process different indoor signals. Outputs of these algorithms are generally used as inputs of data fusion strategies. These methods rely on computationally expensive particle filters, labor-intensive feature analysis, and time-consuming parameter tuning to achieve better accuracies. Besides, particle filters need to estimate the moving directions of particles, limiting smartphone orientation to be stable, and aligned with the user's moving directions. In this paper, we adopted a convolutional neural network (CNN) to implement an accurate and orientation-free positioning system. Inspired by the state-of-the-art image classification methods, we design a novel hybrid location image using Wi-Fi and magnetic field fingerprints, and then a CNN is employed to classify the locations of the fingerprint images. In order to prevent the overfitting problem of the positioning CNN on limited training datasets, we also propose to divide the learning process into two steps to adopt proper learning strategies for different network branches. We show that the CNN solution is able to automatically learn location patterns, thus significantly lower the workforce burden of designing a localization system. Our experimental results convincingly reveal that the proposed positioning method achieves an accuracy of about 1 m under different smartphone orientations, users, and use patterns. |
| first_indexed | 2024-12-17T00:23:42Z |
| format | Article |
| id | doaj.art-4f87bb85a4874cfea9971623a193758b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T00:23:42Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4f87bb85a4874cfea9971623a193758b2022-12-21T22:10:30ZengIEEEIEEE Access2169-35362018-01-016746997471210.1109/ACCESS.2018.28841938554268Indoor Positioning Based on Fingerprint-Image and Deep LearningWenhua Shao0https://orcid.org/0000-0002-2440-9981Haiyong Luo1Fang Zhao2Yan Ma3Zhongliang Zhao4Antonino Crivello5School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing, ChinaBeijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, ChinaSchool of Software Engineering, Beijing University of Posts and Telecommunications, Beijing, ChinaInstitute of Network Technology, Beijing University of Posts and Telecommunications, Beijing, ChinaInstitute of Computer Science, University of Bern, Bern, SwitzerlandInstitute of Information Science and Technologies, Consiglio Nazionale delle Ricerche, Pisa, ItalyWi-Fi and magnetic field fingerprinting have been a hot topic in indoor positioning researches because of their ubiquity and location-related features. Wi-Fi signals can provide rough initial positions, and magnetic fields can further improve the positioning accuracies, therefore many researchers have tried to combine the two signals for high-accuracy indoor localization. Currently, state-of-the-art solutions design separate algorithms to process different indoor signals. Outputs of these algorithms are generally used as inputs of data fusion strategies. These methods rely on computationally expensive particle filters, labor-intensive feature analysis, and time-consuming parameter tuning to achieve better accuracies. Besides, particle filters need to estimate the moving directions of particles, limiting smartphone orientation to be stable, and aligned with the user's moving directions. In this paper, we adopted a convolutional neural network (CNN) to implement an accurate and orientation-free positioning system. Inspired by the state-of-the-art image classification methods, we design a novel hybrid location image using Wi-Fi and magnetic field fingerprints, and then a CNN is employed to classify the locations of the fingerprint images. In order to prevent the overfitting problem of the positioning CNN on limited training datasets, we also propose to divide the learning process into two steps to adopt proper learning strategies for different network branches. We show that the CNN solution is able to automatically learn location patterns, thus significantly lower the workforce burden of designing a localization system. Our experimental results convincingly reveal that the proposed positioning method achieves an accuracy of about 1 m under different smartphone orientations, users, and use patterns.https://ieeexplore.ieee.org/document/8554268/Indoor positioningindoor localizationneural networksfingerprintfeature extraction |
| spellingShingle | Wenhua Shao Haiyong Luo Fang Zhao Yan Ma Zhongliang Zhao Antonino Crivello Indoor Positioning Based on Fingerprint-Image and Deep Learning IEEE Access Indoor positioning indoor localization neural networks fingerprint feature extraction |
| title | Indoor Positioning Based on Fingerprint-Image and Deep Learning |
| title_full | Indoor Positioning Based on Fingerprint-Image and Deep Learning |
| title_fullStr | Indoor Positioning Based on Fingerprint-Image and Deep Learning |
| title_full_unstemmed | Indoor Positioning Based on Fingerprint-Image and Deep Learning |
| title_short | Indoor Positioning Based on Fingerprint-Image and Deep Learning |
| title_sort | indoor positioning based on fingerprint image and deep learning |
| topic | Indoor positioning indoor localization neural networks fingerprint feature extraction |
| url | https://ieeexplore.ieee.org/document/8554268/ |
| work_keys_str_mv | AT wenhuashao indoorpositioningbasedonfingerprintimageanddeeplearning AT haiyongluo indoorpositioningbasedonfingerprintimageanddeeplearning AT fangzhao indoorpositioningbasedonfingerprintimageanddeeplearning AT yanma indoorpositioningbasedonfingerprintimageanddeeplearning AT zhongliangzhao indoorpositioningbasedonfingerprintimageanddeeplearning AT antoninocrivello indoorpositioningbasedonfingerprintimageanddeeplearning |