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Open AccessArticle

Environmental Cross-Validation of NLOS Machine Learning Classification/Mitigation with Low-Cost UWB Positioning Systems

CITIC Research Center, Campus de Elviña, Universidade da Coruña (University of A Coruña), 15071 A Coruña, Spain
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Sensors 2019, 19(24), 5438; https://doi.org/10.3390/s19245438
Received: 12 November 2019 / Revised: 5 December 2019 / Accepted: 7 December 2019 / Published: 10 December 2019
(This article belongs to the Special Issue Sensors Localization in Indoor Wireless Networks)
Indoor positioning systems based on radio frequency inherently present multipath-related phenomena. This causes ranging systems such as ultra-wideband (UWB) to lose accuracy when detecting secondary propagation paths between two devices. If a positioning algorithm uses ranging measurements without considering these phenomena, it will face critical errors in estimating the position. This work analyzes the performance obtained in a localization system when combining location algorithms with machine learning techniques applied to a previous classification and mitigation of the propagation effects. For this purpose, real-world cross-scenarios are considered, where the data extracted from low-cost UWB devices for training the algorithms come from a scenario different from that considered for the test. The experimental results reveal that machine learning (ML) techniques are suitable for detecting non-line-of-sight (NLOS) ranging values in this situation. View Full-Text
Keywords: UWB; machine learning; neural networks; NLOS detection; indoor location algorithms UWB; machine learning; neural networks; NLOS detection; indoor location algorithms
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MDPI and ACS Style

Barral, V.; Escudero, C.J.; García-Naya, J.A.; Suárez-Casal, P. Environmental Cross-Validation of NLOS Machine Learning Classification/Mitigation with Low-Cost UWB Positioning Systems. Sensors 2019, 19, 5438. https://doi.org/10.3390/s19245438

AMA Style

Barral V, Escudero CJ, García-Naya JA, Suárez-Casal P. Environmental Cross-Validation of NLOS Machine Learning Classification/Mitigation with Low-Cost UWB Positioning Systems. Sensors. 2019; 19(24):5438. https://doi.org/10.3390/s19245438

Chicago/Turabian Style

Barral, Valentín; Escudero, Carlos J.; García-Naya, José A.; Suárez-Casal, Pedro. 2019. "Environmental Cross-Validation of NLOS Machine Learning Classification/Mitigation with Low-Cost UWB Positioning Systems" Sensors 19, no. 24: 5438. https://doi.org/10.3390/s19245438

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