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<p>This study introduces QUAPOS, a comprehensive simulated dataset for Visible Light Positioning (VLP) systems using quadrant photodiodes as receivers. Encompassing 73 diverse scenarios, QUAPOS features various receiver-transmitter configurations to facilitate the development and evaluation of novel VLP algorithms. Generated using a novel simulation tool, QUAPOS includes both raw and processed data, such as the received energy at every receiver corresponding to every emitter, the estimated positioning coordinates of the emitters (using our own positioning algorithm and considering that the receivers are in known positions), and the ground truth (real position of the emitters). The data files were stored in .csv and .mat format to ensure their usability. The aim of this dataset is to support research in optimizing receiver placement, developing noise-resistant algorithms, and comparing different VLP system configurations. Furthermore, the dataset stores enough data to apply any type of algorithms, including those based on Machine Learning.</p>

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A Dataset for Assessing and Optimizing Quadrant Photodiode-based Visible Light Positioning Systems

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月12日

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  • Elena Aparicio-Esteve,
  • Jesús Ureña,
  • Álvaro Hernández,
  • David Moltó

摘要

This study introduces QUAPOS, a comprehensive simulated dataset for Visible Light Positioning (VLP) systems using quadrant photodiodes as receivers. Encompassing 73 diverse scenarios, QUAPOS features various receiver-transmitter configurations to facilitate the development and evaluation of novel VLP algorithms. Generated using a novel simulation tool, QUAPOS includes both raw and processed data, such as the received energy at every receiver corresponding to every emitter, the estimated positioning coordinates of the emitters (using our own positioning algorithm and considering that the receivers are in known positions), and the ground truth (real position of the emitters). The data files were stored in .csv and .mat format to ensure their usability. The aim of this dataset is to support research in optimizing receiver placement, developing noise-resistant algorithms, and comparing different VLP system configurations. Furthermore, the dataset stores enough data to apply any type of algorithms, including those based on Machine Learning.