Enhancement of Localization Systems in NLOS Urban Scenario with Multipath Ray Tracing Fingerprints and Machine Learning

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ID: 114757
2018
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Abstract
A hybrid technique is proposed to enhance the localization performance of a time difference of arrival (TDOA) deployed in non-line-of-sight (NLOS) suburban scenario. The idea was to use Machine Learning framework on the dataset, produced by the ray tracing simulation, and the Channel Impulse Response estimation from the real signal received by each sensor. Conventional localization techniques mitigate errors trying to avoid NLOS measurements in processing emitter position, while the proposed method uses the multipath fingerprint information produced by ray tracing (RT) simulation together with calibration emitters to refine a Machine Learning engine, which gives an extra layer of information to improve the emitter position estimation. The ray-tracing fingerprints perform the target localization embedding all the reflection and diffraction in the propagation scenario. A validation campaign was performed and showed the feasibility of the proposed method, provided that the buildings can be appropriately included in the scenario description.
Reference Key
sousa2018sensorsenhancement Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Marcelo N. de Sousa;Reiner S. Thomä;N. de Sousa, Marcelo;S. Thomä, Reiner;
Journal sensors
Year 2018
DOI
10.3390/s18114073
URL
https://www.mdpi.com/1424-8220/18/11/4073/htm
Keywords
Machine learning wireless positioning cooperative positioning hybrid positioning multipath exploitation time difference of arrival localization ray tracing fingerprints National Center for Biotechnology Information NCBI NLM MEDLINE pubmed abstract nih national institutes of health national library of medicine pmid:30469418 pmc6263810 doi:10.3390/s18114073 marcelo n de sousa reiner s thomä

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