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    This study introduces an improved visible light localization method using a thermal map-optimized convolutional neural network. Integrating this with an adaptive federated algorithm significantly enhances indoor navigation accuracy and robustness.

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    Area of Science:

    • Computer Science
    • Electrical Engineering
    • Robotics

    Background:

    • Traditional indoor visible light positioning (VLP) faces accuracy challenges due to signal occlusion, reflection, and noise.
    • Intelligent navigation systems require robust and precise indoor localization solutions.

    Purpose of the Study:

    • To propose a novel VLP method using a thermal map-optimized convolutional neural network (CNN) for enhanced accuracy.
    • To develop an adaptive federated integrated navigation algorithm combining VLP and inertial navigation system (INS) for improved robustness.

    Main Methods:

    • A CNN optimized by thermal maps was employed for VLP, transforming discrete fingerprint coordinates into continuous probability distributions using a Gaussian kernel function.
    • An adaptive federated integrated navigation algorithm was utilized, fusing VLP with INS data.
    • Light intensity characteristics were incorporated into the CNN training process.

    Main Results:

    • The thermal map-optimized CNN method achieved an average positioning error of 5.8 cm.
    • The integrated navigation algorithm combining VLP and INS reduced the average positioning error to 3.7 cm.
    • The integrated system demonstrated enhanced robustness against environmental interference and signal interruptions.

    Conclusions:

    • The proposed thermal map-optimized CNN-based VLP method significantly improves localization accuracy.
    • The adaptive federated integrated navigation algorithm effectively mitigates INS errors and VLP signal issues, enhancing system reliability in complex indoor environments.