Open AccessEmploying DIALux to relieve machine-learning training data collection when designing indoor positioning systems
Author(s) -
Shao-Hua Song,
Dong-Chang Lin,
Yang Liu,
ChiWai Chow,
Yun-Han Chang,
KonPing Lin,
Yichang Wang,
Yiyuan Chen
Publication year - 2021
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.422851
Subject(s) - computer science , data collection , scheme (mathematics) , training (meteorology) , artificial intelligence , training set , position (finance) , software , simulation , machine learning , statistics , mathematics , mathematical analysis , physics , finance , meteorology , economics , programming language
We propose and demonstrate using the DIALux software with our proposed linear-regression machine-learning (LRML) algorithm for designing a practical indoor visible light positioning (VLP) system. Experimental results reveal that the average position errors and error distributions of the model trained via the DIALux simulation and trained via the experimental data match with each other. This implies that the training data can be generated in DIALux if the room dimensions and LED luminary parameters are available. The proposed scheme could relieve the burden of training data collection in VLP systems.