An Efficient Indoor Localization Method Based on the Long Short-Term Memory Recurrent Neuron Network
With the development of deep learning, fingerprints recognition based on neural networks is a widely used method in indoor localization. In this paper, we build a long short-term memory (LSTM) recurrent neuron network to make regression between fingerprints and locations in order to track the moving...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8815691/ |
| _version_ | 1818557546643849216 |
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| author | Bo Xu Xiaorong Zhu Hongbo Zhu |
| author_facet | Bo Xu Xiaorong Zhu Hongbo Zhu |
| author_sort | Bo Xu |
| collection | DOAJ |
| description | With the development of deep learning, fingerprints recognition based on neural networks is a widely used method in indoor localization. In this paper, we build a long short-term memory (LSTM) recurrent neuron network to make regression between fingerprints and locations in order to track the moving target. Simulations are in a BLE5.0 based environment and we use received signal strength indication (RSSI) as the element of fingerprints. Since the preparation of fingerprints is an inevitable and time-consuming process in the testing phase of LSTM, we propose two methods to improve the real-time performance of the localization without changing the structure of LSTM. A decentralized sorting algorithm is proposed to divide the received RSSI signals into multiple parts based on the MAC address of BLE5.0 equipment and use GPUs to sort each part. A complete fingerprint is a combination of these parts. Then, an optimization model aimed at maximum localization accuracy and minimal time used in the testing process of LSTM is proposed by changing the length of fingerprints. Many experiments simulated in different trajectories show that LSTM is more accurate in localization than many other neural networks. Further results demonstrate that using decentralized fingerprints preparation and finding an optimal fingerprint length can keep balance between the localization accuracy and real-time performance. |
| first_indexed | 2024-12-14T00:00:56Z |
| format | Article |
| id | doaj.art-53608ac20fa4456fb759259f8ea74ade |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T00:00:56Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-53608ac20fa4456fb759259f8ea74ade2022-12-21T23:26:20ZengIEEEIEEE Access2169-35362019-01-01712391212392110.1109/ACCESS.2019.29378318815691An Efficient Indoor Localization Method Based on the Long Short-Term Memory Recurrent Neuron NetworkBo Xu0https://orcid.org/0000-0003-4147-0263Xiaorong Zhu1Hongbo Zhu2Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing, ChinaJiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing, ChinaJiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing, ChinaWith the development of deep learning, fingerprints recognition based on neural networks is a widely used method in indoor localization. In this paper, we build a long short-term memory (LSTM) recurrent neuron network to make regression between fingerprints and locations in order to track the moving target. Simulations are in a BLE5.0 based environment and we use received signal strength indication (RSSI) as the element of fingerprints. Since the preparation of fingerprints is an inevitable and time-consuming process in the testing phase of LSTM, we propose two methods to improve the real-time performance of the localization without changing the structure of LSTM. A decentralized sorting algorithm is proposed to divide the received RSSI signals into multiple parts based on the MAC address of BLE5.0 equipment and use GPUs to sort each part. A complete fingerprint is a combination of these parts. Then, an optimization model aimed at maximum localization accuracy and minimal time used in the testing process of LSTM is proposed by changing the length of fingerprints. Many experiments simulated in different trajectories show that LSTM is more accurate in localization than many other neural networks. Further results demonstrate that using decentralized fingerprints preparation and finding an optimal fingerprint length can keep balance between the localization accuracy and real-time performance.https://ieeexplore.ieee.org/document/8815691/Indoor localizationBLE5.0fingerprintreceived signal strength indicationlong short-term memory recurrent neuron network |
| spellingShingle | Bo Xu Xiaorong Zhu Hongbo Zhu An Efficient Indoor Localization Method Based on the Long Short-Term Memory Recurrent Neuron Network IEEE Access Indoor localization BLE5.0 fingerprint received signal strength indication long short-term memory recurrent neuron network |
| title | An Efficient Indoor Localization Method Based on the Long Short-Term Memory Recurrent Neuron Network |
| title_full | An Efficient Indoor Localization Method Based on the Long Short-Term Memory Recurrent Neuron Network |
| title_fullStr | An Efficient Indoor Localization Method Based on the Long Short-Term Memory Recurrent Neuron Network |
| title_full_unstemmed | An Efficient Indoor Localization Method Based on the Long Short-Term Memory Recurrent Neuron Network |
| title_short | An Efficient Indoor Localization Method Based on the Long Short-Term Memory Recurrent Neuron Network |
| title_sort | efficient indoor localization method based on the long short term memory recurrent neuron network |
| topic | Indoor localization BLE5.0 fingerprint received signal strength indication long short-term memory recurrent neuron network |
| url | https://ieeexplore.ieee.org/document/8815691/ |
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