Environment cross validation of NLOS machine learning classification/mitigation in low-cost UWB positioning systems

  1. Barral, Valentín 1
  2. Escudero, Carlos J. 1
  3. García-Naya, José A. 1
  4. Suárez-Casal, Pedro 1
  1. 1 Universidade da Coruña
    info

    Universidade da Coruña

    La Coruña, España

    ROR https://ror.org/01qckj285

Editor: IEEE DataPort

Año de publicación: 2019

Tipo: Dataset

Resumen

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. For this purpose, real cross scenarios are considered, where the data extracted from UWB low-cost devices for the training of the algorithms come from different environments than those considered for the real application and its analysis.