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

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Employing Ray-tracing and Least-Squares Support Vector Machines for Localisation. / Chitambira, Benny; Armour, Simon; Wales, Stephen; Beach, Mark.

In: Sensors, Vol. 18, No. 11, 11.2018.

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@article{9aac4598bf104d2c9bb4b338210181cd,
title = "Employing Ray-tracing and Least-Squares Support Vector Machines for Localisation",
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",
keywords = "localisation, support vector machine, ray-tracing, positioning",
author = "Benny Chitambira and Simon Armour and Stephen Wales and Mark Beach",
year = "2018",
month = "11",
doi = "https://doi.org/10.3390/s18114059",
language = "English",
volume = "18",
journal = "Sensors",
issn = "1424-8220",
publisher = "MDPI AG",
number = "11",

}

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TY - JOUR

T1 - Employing Ray-tracing and Least-Squares Support Vector Machines for Localisation

AU - Chitambira, Benny

AU - Armour, Simon

AU - Wales, Stephen

AU - Beach, Mark

PY - 2018/11

Y1 - 2018/11

N2 - 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

AB - 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

KW - localisation

KW - support vector machine

KW - ray-tracing

KW - positioning

U2 - https://doi.org/10.3390/s18114059

DO - https://doi.org/10.3390/s18114059

M3 - Article

VL - 18

JO - Sensors

JF - Sensors

SN - 1424-8220

IS - 11

ER -