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CSI-based sliding window fingerprinting method tailored for a signal blocking environment in VLP systems.

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    This study introduces a sliding window fingerprinting (SWF) algorithm using channel state information (CSI) to improve visible light indoor positioning accuracy. The SWF method significantly enhances localization performance, even with blocked signals, outperforming traditional algorithms.

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    Area of Science:

    • Electrical Engineering
    • Computer Science
    • Signal Processing

    Background:

    • Visible light indoor positioning systems often suffer from reduced accuracy due to signal occlusion.
    • Received signal strength (RSS)-based fingerprinting algorithms are particularly vulnerable to line-of-sight (LOS) signal blockage.
    • Robust localization methods are needed for reliable indoor navigation.

    Purpose of the Study:

    • To enhance the accuracy and robustness of indoor positioning in visible light systems.
    • To address the performance degradation caused by line-of-sight (LOS) signal occlusion.
    • To propose a novel algorithm that leverages channel state information (CSI) for improved localization.

    Main Methods:

    • A sliding window fingerprinting (SWF) algorithm is developed, combining CSI with sliding matching techniques.
    • The SWF algorithm uses a sliding window to match received CSI with a database, reducing interference from LOS signal loss.
    • A weighted sliding window fingerprinting (W-SWF) variant is introduced to account for varying path contributions in CSI.

    Main Results:

    • The proposed SWF and W-SWF algorithms demonstrate superior performance in a simulated indoor multipath environment.
    • Mean errors of 0.20 cm and 1.43 cm were achieved even when 1 or 2 LED LOS signals were blocked.
    • SWF algorithm showed over 90% improvement in mean error and root mean square error (RMSE) compared to traditional RSS, WKNN, and ARWKNN algorithms.

    Conclusions:

    • The SWF algorithm effectively enhances indoor positioning accuracy and robustness in visible light systems.
    • Leveraging CSI and sliding window matching provides a significant improvement over existing methods, especially under signal occlusion.
    • The proposed W-SWF further refines accuracy by weighting CSI path contributions, offering a promising solution for reliable indoor localization.