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Visible light communication and positioning using positioning cells and machine learning algorithms.

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    This study introduces a visible light positioning system enhanced by machine learning (ML) for improved accuracy. ML algorithms achieved sub-5 cm positioning errors, demonstrating practical application potential.

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    Area of Science:

    • Optical Communications
    • Machine Learning Applications
    • Indoor Positioning Systems

    Background:

    • Visible Light Positioning (VLP) offers a promising alternative for indoor navigation.
    • Existing VLP systems face challenges in achieving high positioning accuracy.
    • Machine learning (ML) presents an opportunity to enhance VLP performance.

    Purpose of the Study:

    • To propose and demonstrate a practical Visible Light Positioning (VLP) system.
    • To enhance VLP accuracy using machine learning (ML) algorithms.
    • To evaluate the performance of different ML models for VLP.

    Main Methods:

    • Implementation of a VLP system utilizing repeated unit cells.
    • Application of machine learning algorithms, specifically 2nd-order regression and polynomial trilateral models.
    • Experimental demonstration and data collection for performance evaluation.

    Main Results:

    • Over 80% of measurements achieved position error within 4 cm using the 2nd-order regression ML model.
    • The polynomial trilateral ML model resulted in position errors within 5 cm.
    • Both ML models demonstrated significant improvements in positioning accuracy.

    Conclusions:

    • The proposed VLP system integrated with ML algorithms is practical and effective.
    • ML algorithms significantly enhance the accuracy of visible light positioning.
    • The 2nd-order regression model shows superior performance for high-accuracy VLP applications.