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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

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.