Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/155172
 Title: WIFI fingerprinting indoor localization using local feature-based deep LSTM Authors: Chen, ZhenghuaZou, HanYang, JianfeiJiang, HaoXie, Lihua Keywords: Engineering::Electrical and electronic engineering Issue Date: 2020 Source: 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 Journal: IEEE Systems Journal Abstract: 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}{\%}. URI: https://hdl.handle.net/10356/155172 ISSN: 1932-8184 DOI: 10.1109/JSYST.2019.2918678 Rights: © 2019 IEEE. All rights reserved. Fulltext Permission: none Fulltext Availability: No Fulltext Appears in Collections: EEE Journal Articles

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