Enhanced Localization Systems with Multipath Fingerprints and Machine Learning

We propose a new method to enhance the performance of radio frequency localization in strong multipath and non-line-of-sight (NLOS) situations. The knowledge about the geometrical structure of multipath propagation environment is exploited by using a ray-tracing tool. We further apply the Random For...

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Bibliographic Details
Published in2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) pp. 1 - 6
Main Authors de Sousa, Marcelo N, Thoma Electronic, Reiner S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2019
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Summary:We propose a new method to enhance the performance of radio frequency localization in strong multipath and non-line-of-sight (NLOS) situations. The knowledge about the geometrical structure of multipath propagation environment is exploited by using a ray-tracing tool. We further apply the Random Forest (RF) algorithm embedded in a machine learning framework to extract a reference data-set of Time Differences of Arrival (TDOA) fingerprints in multipath outdoor scenarios. Site-specific fingerprints are processed with a multidimensional cross-correlation, called Volume Cross-Correlation function (VCC), to extract the multipath features from measurements. The performance and feasibility of our method was evaluated by simulations and measurements.
ISSN:2166-9589
DOI:10.1109/PIMRC.2019.8904120