Indoor positioning systems based on radio frequency systems such as UWB inherently present multipath related phenomena. This causes ranging systems such as UWB}to lose accuracy by detecting secondary propagation paths between two devices. If a positioning algorithm uses ranging measurements without considering these phenomena, it will make important errors in estimating the position. This work analyzes the performance obtained in a localization system when combining location algorithms with machine learning techniques for a previous classification and mitigation of the propagation effects.

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[1] Valentín Barral, Carlos J. Escudero, José A. García-Naya, Pedro Suárez-Casal, "Environment cross validation of NLOS machine learning classification/mitigation in low-cost UWB positioning systems", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/rhhs-fw33. Accessed: Apr. 13, 2024.
@data{rhhs-fw33-19,
doi = {10.21227/rhhs-fw33},
url = {http://dx.doi.org/10.21227/rhhs-fw33},
author = {Valentín Barral; Carlos J. Escudero; José A. García-Naya; Pedro Suárez-Casal },
publisher = {IEEE Dataport},
title = {Environment cross validation of NLOS machine learning classification/mitigation in low-cost UWB positioning systems},
year = {2019} }
TY - DATA
T1 - Environment cross validation of NLOS machine learning classification/mitigation in low-cost UWB positioning systems
AU - Valentín Barral; Carlos J. Escudero; José A. García-Naya; Pedro Suárez-Casal
PY - 2019
PB - IEEE Dataport
UR - 10.21227/rhhs-fw33
ER -
Valentín Barral, Carlos J. Escudero, José A. García-Naya, Pedro Suárez-Casal. (2019). Environment cross validation of NLOS machine learning classification/mitigation in low-cost UWB positioning systems. IEEE Dataport. http://dx.doi.org/10.21227/rhhs-fw33
Valentín Barral, Carlos J. Escudero, José A. García-Naya, Pedro Suárez-Casal, 2019. Environment cross validation of NLOS machine learning classification/mitigation in low-cost UWB positioning systems. Available at: http://dx.doi.org/10.21227/rhhs-fw33.
Valentín Barral, Carlos J. Escudero, José A. García-Naya, Pedro Suárez-Casal. (2019). "Environment cross validation of NLOS machine learning classification/mitigation in low-cost UWB positioning systems." Web.
1. Valentín Barral, Carlos J. Escudero, José A. García-Naya, Pedro Suárez-Casal. Environment cross validation of NLOS machine learning classification/mitigation in low-cost UWB positioning systems [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/rhhs-fw33
Valentín Barral, Carlos J. Escudero, José A. García-Naya, Pedro Suárez-Casal. "Environment cross validation of NLOS machine learning classification/mitigation in low-cost UWB positioning systems." doi: 10.21227/rhhs-fw33