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    Bayesian active learning for received signal strength-based visible light positioning

    Bayesian active learning for received signal strength-based visible light positioning

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    Visible Light Positioning (VLP) is a promising indoor localization technology for providing highly accurate positioning. In this work, a VLP implementation is employed to estimate the position of a vehicle in a room using the Received Signal Strength (RSS) and fixed LED-based light transmitters. Classical VLP approaches use lateration or angulation based on a wireless propagation model to obtain location estimations. However, previous work has shown that machine learning models such as Gaussian processes (GP) achieve better performance and are more robust in general, particularly in presence of non-ideal environmental conditions. As a downside, Machine Learning (ML) models require a large collection of RSS samples, which can be time-consuming to acquire. In this work, a sampling scheme based on active learning (AL) is proposed to automate the vehicle motion and to accelerate the data collection. The scheme is tested on experimental data from a RSS-based VLP setup and compared with different settings to a simple random sampling.

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    Titel :

    Bayesian active learning for received signal strength-based visible light positioning

    Beteiligte:
    Garbuglia, Federico (Autor:in) / Raes, Willem (Autor:in) / De Bruycker, Jorik (Autor:in) / Stevens, Nobby (Autor:in) / Deschrijver, Dirk (Autor:in) / Dhaene, Tom (Autor:in)
    Erscheinungsdatum :

    2022-01-01

    Anmerkungen:

    IEEE PHOTONICS JOURNAL ; ISSN: 1943-0655 ; ISSN: 1943-0647

    DOI :

    https://doi.org/10.1109/JPHOT.2022.3219889

    Medientyp :

    Aufsatz (Zeitschrift)

    Format :

    Elektronische Ressource

    Sprache :

    Englisch

    Schlagwörter :

    Technology and Engineering , INDOOR , ALGORITHM , LOCALIZATION , SYSTEMS , Visible Light Positioning (VLP) , machine learning (ML) , Gaussian , processes (GP) , active learning (AL) , adaptive sampling

    Klassifikation :

    DDC:    006 / 629

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