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http://dx.doi.org/10.11003/JPNT.2022.11.4.361

Performance Comparison of Machine Learning Algorithms for Received Signal Strength-Based Indoor LOS/NLOS Classification of LTE Signals  

Lee, Halim (School of Integrated Technology, Yonsei University)
Seo, Jiwon (School of Integrated Technology, Yonsei University)
Publication Information
Journal of Positioning, Navigation, and Timing / v.11, no.4, 2022 , pp. 361-368 More about this Journal
Abstract
An indoor navigation system that utilizes long-term evolution (LTE) signals has the benefit of no additional infrastructure installation expenses and low base station database management costs. Among the LTE signal measurements, received signal strength (RSS) is particularly appealing because it can be easily obtained with mobile devices. Propagation channel models can be used to estimate the position of mobile devices with RSS. However, conventional channel models have a shortcoming in that they do not discriminate between line-of-sight (LOS) and non-line-of-sight (NLOS) conditions of the received signal. Accordingly, a previous study has suggested separated LOS and NLOS channel models. However, a method for determining LOS and NLOS conditions was not devised. In this study, a machine learning-based LOS/NLOS classification method using RSS measurements is developed. We suggest several machine-learning features and evaluate various machine-learning algorithms. As an indoor experimental result, up to 87.5% classification accuracy was achieved with an ensemble algorithm. Furthermore, the range estimation accuracy with an average error of 13.54 m was demonstrated, which is a 25.3% improvement over the conventional channel model.
Keywords
indoor navigation; machine learning; received signal strength; line-of-sight; non-line-of-sight;
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