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All Journal Engineering, Mathematics and Computer Science Journal (EMACS)
Lie, Maximilianus Maria Kolbe
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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Indoor Positioning System using Gaussian Mixture Model on BLE Fingerprint Lie, Maximilianus Maria Kolbe; Jabar, Bakti Amirul
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 1 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i1.12581

Abstract

After the release of Bluetooth Low Energy (BLE), people have been trying to use Bluetooth as an alternative source to solve indoor positioning. Unfortunately, due to the nature of Bluetooth about proximity, the propagated signal is very fluctuating. This decreases the accuracy considerably and has become one of the main problems in using Bluetooth. To combat the signal fluctuations, we propose a fingerprinting-based concept of using received signal strength (RSS) frequency distribution values as the data in the radio map, which is termed Frequency Distribution Radio Map (FDRM). We also propose a probabilistic fingerprinting-based algorithm utilizing FDRM using Gaussian Mixture Model (GMM) as the probability distribution function (PDF). In the offline phase, we compare 2 methods: k-Means only, and k-Means with Expectation-Maximization (EM); to learn the FDRM. This resulting a probability distribution function (PDF) of the RSS in each reference points for each BLEs. In the online phase, k-Nearest Neighbour (KNN) and weighted average are used to estimate the receiver’s location. The proposed method is validated over 3 different datasets taken from a 4 m x 6 m classroom equipped with chairs and tables. The experiment shows that the proposed fingerprint and model are better in capturing the environment, including the signal fluctuation. By using only k-Means in obtaining the GMM, it achieved mean error of 98.18 cm and standard deviation of 56.11 cm. By adding EM, there will be a trade-off between mean error with better standard deviation and lower computing time. It achieved standard deviation of 47.99 cm and mean error of 112.24 cm.
Co-Authors Jabar, Bakti Amirul