Next Article in Journal
Evaluation of Object Surface Edge Profiles Detected with a 2-D Laser Scanning Sensor
Next Article in Special Issue
Enhancement of Localization Systems in NLOS Urban Scenario with Multipath Ray Tracing Fingerprints and Machine Learning
Previous Article in Journal
Scaling Effect of Fused ASTER-MODIS Land Surface Temperature in an Urban Environment
Previous Article in Special Issue
Reliable Positioning and mmWave Communication via Multi-Point Connectivity
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(11), 4059;

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

Communication Systems & Networks Group, University of Bristol, Bristol BS8 1UB, UK
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
PDF [2849 KB, uploaded 22 November 2018]


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-Text
Keywords: localisation; positioning; ray-tracing; support vector machine localisation; positioning; ray-tracing; support vector machine

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top