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http://dx.doi.org/10.6109/jkiice.2020.24.11.1528

Learning data preprocessing technique for improving indoor positioning performance based on machine learning  

Kim, Dae-Jin (Institute for Image & Cultural Contents, Dongguk University)
Hwang, Chi-Gon (Dept. of Computer Engineering, IIT, Kwangwoon University)
Yoon, Chang-Pyo (Dept. Of Computer & Mobile Convergence, GyeongGi University of Science and Technology)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.11, 2020 , pp. 1528-1533 More about this Journal
Abstract
Recently, indoor location recognition technology using Wi-Fi fingerprints has been applied and operated in various industrial fields and public services. Along with the interest in machine learning technology, location recognition technology based on machine learning using wireless signal data around a terminal is rapidly developing. At this time, in the process of collecting radio signal data required for machine learning, the accuracy of location recognition is lowered due to distorted or unsuitable data for learning. In addition, when location recognition is performed based on data collected at a specific location, a problem occurs in location recognition at surrounding locations that are not included in the learning. In this paper, we propose a learning data preprocessing technique to obtain an improved position recognition result through the preprocessing of the collected learning data.
Keywords
Machine Learning; Indoor Positioning; Wi-Fi Fingerprint; Random Forest;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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