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Sensors 2018, 18(11), 4073; https://doi.org/10.3390/s18114073

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

Institute for Information Technology, Technische Universität Ilmenau, P.O. Box 100565, D-98684 Ilmenau, Germany
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This paper is an extended version of our paper published in 2018 8th International Conference on Localization and GNSS (ICL-GNSS), Guimaraes, Portugal, 26–28 June 2018.
Received: 5 September 2018 / Revised: 17 November 2018 / Accepted: 18 November 2018 / Published: 21 November 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. View Full-Text
Keywords: wireless positioning; cooperative positioning; machine learning; hybrid positioning; multipath exploitation; time difference of arrival localization; ray tracing fingerprints wireless positioning; cooperative positioning; machine learning; hybrid positioning; multipath exploitation; time difference of arrival localization; ray tracing fingerprints
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N. de Sousa, M.; S. Thomä, R. Enhancement of Localization Systems in NLOS Urban Scenario with Multipath Ray Tracing Fingerprints and Machine Learning. Sensors 2018, 18, 4073.

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