AI_based_Indoor_Positioning_TechRix.pdf (1.73 MB)

Machine Learning aided Precise Indoor Positioning

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posted on 2022-06-13, 19:42 authored by Zihuai LinZihuai Lin

This study describes a UWB and Machine Learning (ML)-based indoor positioning system. We propose a simple mathematical strategy to create data to reduce the job of measurements for fingerprint-based indoor localization systems. A considerable number of measurements can be avoided this way. The paper compares and contrasts the performance of four distinct models. Most test locations’ average error may be reduced to less than 150 mm using the best model.


Email Address of Submitting Author

Submitting Author's Institution

University of Sydney

Submitting Author's Country

  • Australia