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Related Experiment Video

Updated: Jun 20, 2026

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

Elena Aparicio-Esteve1, Jesús Ureña2, Álvaro Hernández2

  • 1University of Alcalá, Electronics Department, Alcalá de Henares, 28801, Spain. elena.aparicio@uah.es.

Scientific Data
|April 12, 2025
PubMed
Summary
This summary is machine-generated.

This study presents QUAPOS, a new simulated dataset for Visible Light Positioning (VLP) systems. It aids in developing and evaluating VLP algorithms using quadrant photodiodes, supporting research in receiver placement and noise-resistant methods.

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Area of Science:

  • Robotics and Automation
  • Optical Engineering
  • Computer Science

Background:

  • Visible Light Positioning (VLP) systems offer a promising alternative for indoor localization.
  • Quadrant photodiodes are increasingly utilized as receivers in VLP systems due to their sensitivity and cost-effectiveness.
  • The development of robust VLP algorithms requires comprehensive and diverse datasets for training and validation.

Purpose of the Study:

  • To introduce QUAPOS, a novel and comprehensive simulated dataset specifically designed for Visible Light Positioning (VLP) systems.
  • To provide a versatile resource for researchers to develop, evaluate, and compare VLP algorithms.
  • To support advancements in optimizing receiver placement, enhancing noise resistance, and exploring different VLP system configurations.

Main Methods:

  • Generation of a simulated dataset (QUAPOS) using a custom simulation tool.
  • Inclusion of 73 diverse scenarios with varying receiver-transmitter configurations.
  • Data provided in .csv and .mat formats, including raw and processed information such as received energy, estimated emitter positions, and ground truth.

Main Results:

  • QUAPOS dataset encompasses raw and processed data essential for VLP algorithm development.
  • The dataset includes detailed information on received energy, estimated emitter coordinates, and ground truth positions.
  • Data is formatted for broad usability, supporting various algorithmic approaches, including machine learning.

Conclusions:

  • QUAPOS serves as a valuable resource for the VLP research community.
  • The dataset facilitates the development of more accurate and reliable VLP systems.
  • It enables further investigation into optimizing VLP system performance and exploring new algorithmic solutions.