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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Published in | 2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) pp. 1 - 6 |
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Main Authors | , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.09.2019
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Subjects | |
Online Access | Get full text |
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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. |
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ISSN: | 2166-9589 |
DOI: | 10.1109/PIMRC.2019.8904120 |