Next Article in Journal
Blind Spoofing GNSS Constellation Detection Using a Multi-Antenna Snapshot Receiver
Next Article in Special Issue
A Robust PDR/UWB Integrated Indoor Localization Approach for Pedestrians in Harsh Environments
Previous Article in Journal
Multi-Target Localization and Tracking Using TDOA and AOA Measurements Based on Gibbs-GLMB Filtering
Previous Article in Special Issue
Multiple Simultaneous Ranging in IR-UWB Networks
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
*
Author to whom correspondence should be addressed.
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
Show Figures

Graphical abstract

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop