LOS/NLOS Classification Using Scenario-Dependent Unsupervised Machine Learning
Kirmaz A, Michalopoulos DS, Balan I, Gerstacker W (2021)
Publication Type: Conference contribution
Publication year: 2021
Publisher: Institute of Electrical and Electronics Engineers Inc.
Book Volume: 2021-September
Pages Range: 1134-1140
Conference Proceedings Title: IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC
Event location: Virtual, Helsinki, FIN
ISBN: 9781728175867
DOI: 10.1109/PIMRC50174.2021.9569316
Abstract
Location information is essential for a wide range of applications requiring, for example, highly accurate positioning information such as industrial automation, autonomous driving, inbound logistics and augmented reality. One of the major error sources in positioning is non-line-of-sight (NLOS) propagation while a line-of-sight (LOS) propagation is anticipated. Therefore, classifying positioning measurements as LOS or NLOS plays a key role to enable high accuracy positioning use cases.Existing solutions for the classification task either rely on availability of label or reference measurement data or do not consider effects of different scenarios or channel models. In this paper, we propose an unsupervised method for the classification task through a channel feature selection process to select only useful channel features to be used for the classification. Then, we show based on real-world data from several measurement campaigns that the proposed method outperforms the existing solutions both in classification performance and ranging accuracy.
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APA:
Kirmaz, A., Michalopoulos, D.S., Balan, I., & Gerstacker, W. (2021). LOS/NLOS Classification Using Scenario-Dependent Unsupervised Machine Learning. In IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC (pp. 1134-1140). Virtual, Helsinki, FIN: Institute of Electrical and Electronics Engineers Inc..
MLA:
Kirmaz, Anil, et al. "LOS/NLOS Classification Using Scenario-Dependent Unsupervised Machine Learning." Proceedings of the 32nd IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2021, Virtual, Helsinki, FIN Institute of Electrical and Electronics Engineers Inc., 2021. 1134-1140.
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