Barral Vales, ValentínEscudero, Carlos J.García-Naya, José A.Suárez-Casal, Pedro2020-01-142020-01-142019-12-10Barral, 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, 5438.1424-8220http://hdl.handle.net/2183/24632[Abstract] 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.engAtribución 4.0 Españahttp://creativecommons.org/licenses/by/4.0/es/UWBMachine learningNeural networksNLOS detectionIndoor location algorithmsjournal articleopen access10.3390/s19245438