Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation†
AbstractThis 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. View Full-Text
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Chitambira, B.; Armour, S.; Wales, S.; Beach, M. Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation. Sensors 2018, 18, 4059.
Chitambira B, Armour S, Wales S, Beach M. Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation. Sensors. 2018; 18(11):4059.Chicago/Turabian Style
Chitambira, Benny; Armour, Simon; Wales, Stephen; Beach, Mark. 2018. "Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation." Sensors 18, no. 11: 4059.
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