Sensors 2018, 18(11), 4059; https://doi.org/10.3390/s18114059
Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation†
1
Communication Systems & Networks Group, University of Bristol, Bristol BS8 1UB, UK
2
Roke Manor Research, Romsey, Hampshire SO51 0ZN, UK
*
Author to whom correspondence should be addressed.
†
This article is an extended version of the conference paper published in Chitambira, B.; Armour, S.; Wales, S.; Beach, M. Direct Localisation using Ray-tracing and Least-Squares Support Vector Machines. In Proceedings of the 8th International Conference on Localization and GNSS (ICL-GNSS) in Guimaraes, Portugal, 26–28 June 2018, doi:10.1109/ICLGNSS.2018.8440915.
Received: 30 September 2018 / Revised: 13 November 2018 / Accepted: 17 November 2018 / Published: 20 November 2018
(This article belongs to the Special Issue Selected Papers from 8th International Conference on Localization and GNSS 2018 (ICL-GNSS 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. View Full-TextKeywords:
localisation; positioning; ray-tracing; support vector machine
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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.
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