Employing DIALux to relieve machine-learning training data collection when designing indoor positioning systems
Shao-Hua Song, Dong-Chang Lin, Yang Liu, Chi‐Wai Chow, Yun-Han Chang, Kun-Hsien Lin, Yichang Wang, Yiyuan Chen
Abstract
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.
Topics & Concepts
Computer scienceData collectionTraining (meteorology)Scheme (mathematics)Position (finance)Training setArtificial intelligenceSoftwareSimulationMachine learningStatisticsMathematicsMeteorologyMathematical analysisFinanceEconomicsPhysicsProgramming languageOptical Wireless Communication TechnologiesIndoor and Outdoor Localization TechnologiesRadio Wave Propagation Studies