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Employing Ray-tracing and Least-Squares Support Vector Machines for Localisation

Research output: Contribution to journalArticle

Original languageEnglish
JournalSensors
Volume18
Issue number11
Early online date20 Nov 2018
DOIs
DateAccepted/In press - 17 Nov 2018
DateE-pub ahead of print - 20 Nov 2018
DatePublished (current) - Nov 2018

Abstract

This article evaluates the use of least-squares support vector machines, with ray-traced data, to solve the problem of localisation in multipath environments. The schemes discussed concern 2-D localisation, but could easily be extended to 3-D. It does not require NLOS identification and mitigation, hence, it can be applied in any environment. Some background details and a detailed experimental setup is provided. Comparisons with schemes that require NLOS identification and mitigation, from earlier work, are also presented. The results demonstrate that the direct localisation scheme using least-squares support vector machine (the Direct method) achieves superior outage to TDOA and TOA/AOA for NLOS environments. TDOA has better outage in LOS environments. TOA/AOA performs better for an accepted outage probability of 20 percent or greater but as the outage probability lowers, the Direct method becomes better

    Research areas

  • localisation, support vector machine, ray-tracing, positioning

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    Rights statement: This is the final published version of the article (version of record). It first appeared online via MDPI at https://www.mdpi.com/1424-8220/18/11/4059 . Please refer to any applicable terms of use of the publisher.

    Final published version, 2 MB, PDF document

    Licence: CC BY

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