Attention Mechanism and LSTM Network for Fingerprint-Based Indoor Location System
The demand for precise indoor localization services is steadily increasing. Among various methods, fingerprint-based indoor localization has become a popular choice due to its exceptional accuracy, cost-effectiveness, and ease of implementation. However, its performance degrades significantly as a r...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/5/1398 |
| _version_ | 1797263856696819712 |
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| author | Zhen Wu Peng Hu Shuangyue Liu Tao Pang |
| author_facet | Zhen Wu Peng Hu Shuangyue Liu Tao Pang |
| author_sort | Zhen Wu |
| collection | DOAJ |
| description | The demand for precise indoor localization services is steadily increasing. Among various methods, fingerprint-based indoor localization has become a popular choice due to its exceptional accuracy, cost-effectiveness, and ease of implementation. However, its performance degrades significantly as a result of multipath signal attenuation and environmental changes. In this paper, we propose an indoor localization method based on fingerprints using self-attention and long short-term memory (LSTM). By integrating a self-attention mechanism and LSTM network, the proposed method exhibits outstanding positioning accuracy and robustness in diverse experimental environments. The performance of the proposed method is evaluated under two different experimental scenarios, which involve 2D and 3D moving trajectories, respectively. The experimental results demonstrate that our approach achieves an average localization error of 1.76 m and 2.83 m in the respective scenarios, outperforming the existing state-of-the-art methods by 42.67% and 31.64%. |
| first_indexed | 2024-04-25T00:19:39Z |
| format | Article |
| id | doaj.art-102c80820663499784b7377e17cabf3a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-25T00:19:39Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-102c80820663499784b7377e17cabf3a2024-03-12T16:54:37ZengMDPI AGSensors1424-82202024-02-01245139810.3390/s24051398Attention Mechanism and LSTM Network for Fingerprint-Based Indoor Location SystemZhen Wu0Peng Hu1Shuangyue Liu2Tao Pang3Department of Mobile Communications and Terminal Research, China Telecom Research Institute, Guangzhou 510000, ChinaDepartment of Mobile Communications and Terminal Research, China Telecom Research Institute, Guangzhou 510000, ChinaDepartment of Mobile Communications and Terminal Research, China Telecom Research Institute, Guangzhou 510000, ChinaDepartment of Mobile Communications and Terminal Research, China Telecom Research Institute, Guangzhou 510000, ChinaThe demand for precise indoor localization services is steadily increasing. Among various methods, fingerprint-based indoor localization has become a popular choice due to its exceptional accuracy, cost-effectiveness, and ease of implementation. However, its performance degrades significantly as a result of multipath signal attenuation and environmental changes. In this paper, we propose an indoor localization method based on fingerprints using self-attention and long short-term memory (LSTM). By integrating a self-attention mechanism and LSTM network, the proposed method exhibits outstanding positioning accuracy and robustness in diverse experimental environments. The performance of the proposed method is evaluated under two different experimental scenarios, which involve 2D and 3D moving trajectories, respectively. The experimental results demonstrate that our approach achieves an average localization error of 1.76 m and 2.83 m in the respective scenarios, outperforming the existing state-of-the-art methods by 42.67% and 31.64%.https://www.mdpi.com/1424-8220/24/5/1398fingerprintingindoor localization systemlong short-term memory (LSTM)self-attention mechanism |
| spellingShingle | Zhen Wu Peng Hu Shuangyue Liu Tao Pang Attention Mechanism and LSTM Network for Fingerprint-Based Indoor Location System Sensors fingerprinting indoor localization system long short-term memory (LSTM) self-attention mechanism |
| title | Attention Mechanism and LSTM Network for Fingerprint-Based Indoor Location System |
| title_full | Attention Mechanism and LSTM Network for Fingerprint-Based Indoor Location System |
| title_fullStr | Attention Mechanism and LSTM Network for Fingerprint-Based Indoor Location System |
| title_full_unstemmed | Attention Mechanism and LSTM Network for Fingerprint-Based Indoor Location System |
| title_short | Attention Mechanism and LSTM Network for Fingerprint-Based Indoor Location System |
| title_sort | attention mechanism and lstm network for fingerprint based indoor location system |
| topic | fingerprinting indoor localization system long short-term memory (LSTM) self-attention mechanism |
| url | https://www.mdpi.com/1424-8220/24/5/1398 |
| work_keys_str_mv | AT zhenwu attentionmechanismandlstmnetworkforfingerprintbasedindoorlocationsystem AT penghu attentionmechanismandlstmnetworkforfingerprintbasedindoorlocationsystem AT shuangyueliu attentionmechanismandlstmnetworkforfingerprintbasedindoorlocationsystem AT taopang attentionmechanismandlstmnetworkforfingerprintbasedindoorlocationsystem |