WIFI fingerprinting indoor localization using local feature-based deep LSTM
Indoor localization has attracted more and more attention because of its importance in many applications. One of the most popular techniques for indoor localization is the received signal strength indicator (RSSI) based fingerprinting approach. Since RSSI values are very complicated and noisy, conve...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/155172 |
| _version_ | 1811690048185171968 |
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| author | Chen, Zhenghua Zou, Han Yang, Jianfei Jiang, Hao Xie, Lihua |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Chen, Zhenghua Zou, Han Yang, Jianfei Jiang, Hao Xie, Lihua |
| author_sort | Chen, Zhenghua |
| collection | NTU |
| description | Indoor localization has attracted more and more attention because of its importance in many applications. One of the most popular techniques for indoor localization is the received signal strength indicator (RSSI) based fingerprinting approach. Since RSSI values are very complicated and noisy, conventional machine learning algorithms often suffer from limited performance. Recently developed deep learning algorithms have been shown to be powerful for the analysis of complex data. In this paper, we propose a local feature-based deep long short-term memory (LF-DLSTM) approach for WiFi fingerprinting indoor localization. The local feature extractor attempts to reduce the noise effect and extract robust local features. The DLSTM network is able to encode temporal dependencies and learn high-level representations for the extracted sequential local features. Real experiments have been conducted in two different environments, i.e., a research lab and an office. We also compare the proposed approach with some state-of-the-art methods for indoor localization. The results show that the proposed approach achieves the best localization performance with mean localization errors of 1.48 and 1.75 m under the research lab and office environments, respectively. The improvements of our proposed approach over the state-of-the-art methods range from \text{18.98}{\%} to \text{53.46}{\%}. |
| first_indexed | 2024-10-01T05:57:48Z |
| format | Journal Article |
| id | ntu-10356/155172 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:57:48Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1551722022-02-15T05:50:58Z WIFI fingerprinting indoor localization using local feature-based deep LSTM Chen, Zhenghua Zou, Han Yang, Jianfei Jiang, Hao Xie, Lihua School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Deep Learning WiFi Fingerprinting Indoor localization has attracted more and more attention because of its importance in many applications. One of the most popular techniques for indoor localization is the received signal strength indicator (RSSI) based fingerprinting approach. Since RSSI values are very complicated and noisy, conventional machine learning algorithms often suffer from limited performance. Recently developed deep learning algorithms have been shown to be powerful for the analysis of complex data. In this paper, we propose a local feature-based deep long short-term memory (LF-DLSTM) approach for WiFi fingerprinting indoor localization. The local feature extractor attempts to reduce the noise effect and extract robust local features. The DLSTM network is able to encode temporal dependencies and learn high-level representations for the extracted sequential local features. Real experiments have been conducted in two different environments, i.e., a research lab and an office. We also compare the proposed approach with some state-of-the-art methods for indoor localization. The results show that the proposed approach achieves the best localization performance with mean localization errors of 1.48 and 1.75 m under the research lab and office environments, respectively. The improvements of our proposed approach over the state-of-the-art methods range from \text{18.98}{\%} to \text{53.46}{\%}. 2022-02-11T08:25:39Z 2022-02-11T08:25:39Z 2020 Journal Article Chen, Z., Zou, H., Yang, J., Jiang, H. & Xie, L. (2020). WIFI fingerprinting indoor localization using local feature-based deep LSTM. IEEE Systems Journal, 14(2), 3001-3010. https://dx.doi.org/10.1109/JSYST.2019.2918678 1932-8184 https://hdl.handle.net/10356/155172 10.1109/JSYST.2019.2918678 2-s2.0-85086067352 2 14 3001 3010 en IEEE Systems Journal © 2019 IEEE. All rights reserved. |
| spellingShingle | Engineering::Electrical and electronic engineering Deep Learning WiFi Fingerprinting Chen, Zhenghua Zou, Han Yang, Jianfei Jiang, Hao Xie, Lihua WIFI fingerprinting indoor localization using local feature-based deep LSTM |
| title | WIFI fingerprinting indoor localization using local feature-based deep LSTM |
| title_full | WIFI fingerprinting indoor localization using local feature-based deep LSTM |
| title_fullStr | WIFI fingerprinting indoor localization using local feature-based deep LSTM |
| title_full_unstemmed | WIFI fingerprinting indoor localization using local feature-based deep LSTM |
| title_short | WIFI fingerprinting indoor localization using local feature-based deep LSTM |
| title_sort | wifi fingerprinting indoor localization using local feature based deep lstm |
| topic | Engineering::Electrical and electronic engineering Deep Learning WiFi Fingerprinting |
| url | https://hdl.handle.net/10356/155172 |
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