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

Clicks: 233
ID: 110647
2018
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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.
Reference Key
chitambira2018sensorsemploying Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Benny Chitambira;Simon Armour;Stephen Wales;Mark Beach;Chitambira, Benny;Armour, Simon;Wales, Stephen;Beach, Mark;
Journal sensors
Year 2018
DOI
10.3390/s18114059
URL
https://doi.org/10.3390/s18114059##overview
Keywords
support vector machine positioning localisation ray-tracing National Center for Biotechnology Information NCBI NLM MEDLINE pubmed abstract nih national institutes of health national library of medicine pmid:30463397 pmc6263922 doi:10.3390/s18114059 benny chitambira simon armour mark beach

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