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Research Article

An Efficient Method for BLE Indoor Localization Using Signal Fingerprint

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  • @ARTICLE{10.4108/eetinis.v12i1.6571,
        author={Trong-Thanh Han and Phuc Nguyen Dinh and Toan Nguyen Duc and Vu Nguyen Long and Hung Dinh Tan},
        title={An Efficient Method for BLE Indoor Localization Using Signal Fingerprint},
        journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems},
        volume={12},
        number={1},
        publisher={EAI},
        journal_a={INIS},
        year={2024},
        month={11},
        keywords={Indoor Localization, Fingerprint, Bluetooth Low Energy, Autoencoder},
        doi={10.4108/eetinis.v12i1.6571}
    }
    
  • Trong-Thanh Han
    Phuc Nguyen Dinh
    Toan Nguyen Duc
    Vu Nguyen Long
    Hung Dinh Tan
    Year: 2024
    An Efficient Method for BLE Indoor Localization Using Signal Fingerprint
    INIS
    EAI
    DOI: 10.4108/eetinis.v12i1.6571
Trong-Thanh Han1,*, Phuc Nguyen Dinh1, Toan Nguyen Duc1, Vu Nguyen Long1, Hung Dinh Tan1
  • 1: Hanoi University of Science and Technology
*Contact email: thanh.hantrong@set.hust.edu.vn

Abstract

The rise of Bluetooth Low Energy (BLE) technology has opened new possibilities for indoor localization systems. However, extracting fingerprint features from the Received Signal Strength Indicator (RSSI) of BLE signals often encounters challenges due to significant errors and fluctuations. This research proposes an approach that integrates signal filtering and deep learning techniques to improve accuracy and stability. A Kalman filter is employed to smooth the RSSI values, while Autoencoder and Convolutional Autoencoder models are utilized to extract distinctive fingerprint features. The system compares random test points with a reference database using normalized cross-correlation. Performance is assessed based on metrics such as the number of reference points with the highest cross-correlation (), average localization error, and other statistical indicators. Experimental results show that the combination of the Kalman filter with the Convolutional Autoencoder model achieves an average error of 0.98 meters with . These findings indicate that this approach effectively reduces signal noise and enhances localization accuracy in indoor environments.

Keywords
Indoor Localization, Fingerprint, Bluetooth Low Energy, Autoencoder
Received
2024-11-25
Accepted
2024-11-25
Published
2024-11-25
Publisher
EAI
http://dx.doi.org/10.4108/eetinis.v12i1.6571

Copyright © 2024 Trong-Thanh Han et al., licensed to EAI. This is an open access article distributed under the terms of the CC BYNC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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