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

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

Benny Chitambira;Simon Armour;Stephen Wales;Mark Beach;Chitambira, Benny;Armour, Simon;Wales, Stephen;Beach, Mark;
sensors 2018 Vol. 18 pp. 4059-
229
chitambira2018sensorsemploying

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.

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ID: 110647
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