Online Learning-Based Adaptive Device-Free Localization in Time-Varying Indoor Environment

With the widespread use of WiFi devices and the availability of channel state information (CSI), CSI-based device-free localization (DFL) has attracted lots of attention. Fingerprint-based localization methods are the primary solutions for DFL, but they are faced with the fingerprint similarity prob...

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Bibliographic Details
Published in:Applied sciences 2024-01, Vol.14 (2), p.643
Main Authors: Xue, Jianqiang, Chen, Xingcan, Chi, Qingyun, Xiao, Wendong
Format: Article
Language:English
Subjects:
Accuracy
channel state information (CSI)
Deep learning
Design
device-free localization (DFL)
Distance learning
environmental changes
fingerprint similarity problem
Global positioning systems
GPS
Localization
Methods
Multilayer Online Sequence Extreme Learning Machine (ML-OSELM)
Online education
Parameter estimation
Propagation
Signal processing
Wi-Fi
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Summary:With the widespread use of WiFi devices and the availability of channel state information (CSI), CSI-based device-free localization (DFL) has attracted lots of attention. Fingerprint-based localization methods are the primary solutions for DFL, but they are faced with the fingerprint similarity problem due to the complex environment and low bandwidth of the commercial WiFi. Meanwhile, fingerprints may change unpredictably due to multipath WiFi signal propagation in time-varying environments. To tackle these problems, this paper proposes an adaptive online learning DFL method, which adaptively updates the localization model to ensure long-term accuracy and adaptability. Specifically, the CSI signals of the target located at different reference points are first collected and transformed to discriminable fingerprints using the weights of Multilayer Online Sequence Extreme Learning Machine (ML-OSELM). After that, an online learning DFL model is built to adapt to the changes of the environment. Experimental results in a time-varying indoor environment validate the adaptability of the proposed method against environmental changes and show that our method can achieve 10% improvement over other methods.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14020643