Hierarchical KNN for Smartphone-Based 3D Indoor Positioning

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dc.contributor.authorAdiyatma, Farid Yuli Martin
dc.contributor.authorSunimit, Samita
dc.contributor.authorChokporntaveesuk, Thanwa
dc.contributor.authorLualum, Krittima
dc.contributor.authorChaisang, Naphat
dc.contributor.authorCherntanomwong, Panarat
dc.date.accessioned2026-08-06T10:43:57Z
dc.date.available2026-08-06T10:43:57Z
dc.date.issued2024-01-01
dc.description.abstractFingerprint-based localization, or positioning technique, is well-known to achieve high accuracy in location estimation in indoor environments where the multipath fading effect is severe. However, the accuracy of location estimation depends on the choice of the pattern matching techniques that are developed in the on-line phase. This paper proposes a new algorithm called Hierarchical K-Nearest Neighbors (KNN) for the pattern matching phase to estimate the location of the target in 3-dimensional (3D) indoor environments. For practical usage and saving budget and time for implementation, the Wi-Fi-based indoor positioning system (IPS) is implemented, and the smartphone is used as the user device. In this work, an Android smartphone is used for the study case. The results demonstrate that Hierarchical KNN achieves the lowest mean distance error (MDE) of approximately 3.263 m, outperforming various fundamental machine learning approaches such as Random Forest and KNN classifiers, with MDE reductions of 8.19% and 11.52%, respectively.
dc.identifier.citation2024 International Technical Conference on Circuits Systems Computers and Communications Itc Cscc 2024, 2024
dc.identifier.doi10.1109/ITC-CSCC62988.2024.10628267
dc.identifier.other2-s2.0-85203594471
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/15072
dc.source2024 International Technical Conference on Circuits Systems Computers and Communications Itc Cscc 2024
dc.subjectHierarchical KNN
dc.subjectIndoor Positioning
dc.subjectSmartphone
dc.subjectWi-Fi
dc.titleHierarchical KNN for Smartphone-Based 3D Indoor Positioning
dc.typeConference Paper

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