Geomagnetic Field Based Indoor Landmark Classification Using Deep Learning
The unstable nature of radio frequency signals and the need for external infrastructure inside buildings have limited the use of positioning techniques, such as Wi-Fi and Bluetooth fingerprinting. Compared to these techniques, the geomagnetic field exhibits stable signal strength in the time domain....
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| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8660396/ |
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| author | Bimal Bhattarai Rohan Kumar Yadav Hui-Seon Gang Jae-Young Pyun |
| author_facet | Bimal Bhattarai Rohan Kumar Yadav Hui-Seon Gang Jae-Young Pyun |
| author_sort | Bimal Bhattarai |
| collection | DOAJ |
| description | The unstable nature of radio frequency signals and the need for external infrastructure inside buildings have limited the use of positioning techniques, such as Wi-Fi and Bluetooth fingerprinting. Compared to these techniques, the geomagnetic field exhibits stable signal strength in the time domain. However, existing magnetic positioning methods cannot perform well in a wide space because the magnetic signal is not always discernible. In this paper, we introduce deep recurrent neural networks (DRNNs) to build a model that is capable of capturing long-range dependencies in variable-length input sequences. The use of DRNNs is brought from the idea that the spatial/temporal sequence of magnetic field values around a given area will create a unique pattern over time, despite multiple locations having the same magnetic field value. Therefore, we can divide the indoor space into landmarks with magnetic field values and find the position of the user in a particular area inside the building. We present long short-term memory DRNNs for spatial/temporal sequence learning of magnetic patterns and evaluate their positioning performance on our testbed datasets. The experimental results show that our proposed models outperform other traditional positioning approaches with machine learning methods, such as support vector machine and k-nearest neighbors. |
| format | Article |
| id | doaj-art-e04267721b904f3b96baa8c27c72d949 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e04267721b904f3b96baa8c27c72d9492025-01-16T00:01:00ZengIEEEIEEE Access2169-35362019-01-017339433395610.1109/ACCESS.2019.29025738660396Geomagnetic Field Based Indoor Landmark Classification Using Deep LearningBimal Bhattarai0https://orcid.org/0000-0002-7339-3621Rohan Kumar Yadav1https://orcid.org/0000-0003-1485-0439Hui-Seon Gang2Jae-Young Pyun3Department of Information Communication Engineering, Chosun University, Gwangju, South KoreaDepartment of Information Communication Engineering, Chosun University, Gwangju, South KoreaDepartment of Information Communication Engineering, Chosun University, Gwangju, South KoreaDepartment of Information Communication Engineering, Chosun University, Gwangju, South KoreaThe unstable nature of radio frequency signals and the need for external infrastructure inside buildings have limited the use of positioning techniques, such as Wi-Fi and Bluetooth fingerprinting. Compared to these techniques, the geomagnetic field exhibits stable signal strength in the time domain. However, existing magnetic positioning methods cannot perform well in a wide space because the magnetic signal is not always discernible. In this paper, we introduce deep recurrent neural networks (DRNNs) to build a model that is capable of capturing long-range dependencies in variable-length input sequences. The use of DRNNs is brought from the idea that the spatial/temporal sequence of magnetic field values around a given area will create a unique pattern over time, despite multiple locations having the same magnetic field value. Therefore, we can divide the indoor space into landmarks with magnetic field values and find the position of the user in a particular area inside the building. We present long short-term memory DRNNs for spatial/temporal sequence learning of magnetic patterns and evaluate their positioning performance on our testbed datasets. The experimental results show that our proposed models outperform other traditional positioning approaches with machine learning methods, such as support vector machine and k-nearest neighbors.https://ieeexplore.ieee.org/document/8660396/Deep recurrent neural network (DRNN)fingerprintinggeomagnetic fieldlong short-term memory (LSTM) |
| spellingShingle | Bimal Bhattarai Rohan Kumar Yadav Hui-Seon Gang Jae-Young Pyun Geomagnetic Field Based Indoor Landmark Classification Using Deep Learning IEEE Access Deep recurrent neural network (DRNN) fingerprinting geomagnetic field long short-term memory (LSTM) |
| title | Geomagnetic Field Based Indoor Landmark Classification Using Deep Learning |
| title_full | Geomagnetic Field Based Indoor Landmark Classification Using Deep Learning |
| title_fullStr | Geomagnetic Field Based Indoor Landmark Classification Using Deep Learning |
| title_full_unstemmed | Geomagnetic Field Based Indoor Landmark Classification Using Deep Learning |
| title_short | Geomagnetic Field Based Indoor Landmark Classification Using Deep Learning |
| title_sort | geomagnetic field based indoor landmark classification using deep learning |
| topic | Deep recurrent neural network (DRNN) fingerprinting geomagnetic field long short-term memory (LSTM) |
| url | https://ieeexplore.ieee.org/document/8660396/ |
| work_keys_str_mv | AT bimalbhattarai geomagneticfieldbasedindoorlandmarkclassificationusingdeeplearning AT rohankumaryadav geomagneticfieldbasedindoorlandmarkclassificationusingdeeplearning AT huiseongang geomagneticfieldbasedindoorlandmarkclassificationusingdeeplearning AT jaeyoungpyun geomagneticfieldbasedindoorlandmarkclassificationusingdeeplearning |