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

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 VL...

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Detalles Bibliográficos
Autores: Aparicio Esteve, Elena|||0000-0001-7886-312X, Ureña Ureña, Jesús|||0000-0003-1408-6039, Hernández Alonso, Álvaro|||0000-0001-9308-8133, Moltó Orozco, David|||0000-0002-2790-3758
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad de Alcalá (UAH)
Repositorio:e_Buah Biblioteca Digital Universidad de Alcalá
Idioma:inglés
OAI Identifier:oai:ebuah.uah.es:10017/67556
Acceso en línea:http://hdl.handle.net/10017/67556
https://dx.doi.org/10.1038/s41597-025-04929-6
Access Level:acceso abierto
Palabra clave:Electrónica
Electronics
Descripción
Sumario: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.