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
†
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.
*
Author to whom correspondence should be addressed.
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
▼
Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
Share & Cite This Article
MDPI and ACS Style
Chitambira, B.; Armour, S.; Wales, S.; Beach, M. Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation. Sensors 2018, 18, 4059.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.