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

Clicks: 272
ID: 111465
2018
Free PDF
Article Quality & Performance Metrics
Overall Quality Improving Quality
0.0 /100
Combines engagement data with AI-assessed academic quality
AI Quality Assessment
Not analyzed
Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
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, …
Reference Key
b2018sensorsemploying Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chitambira B;Armour S;Wales S;Beach M;;
Journal sensors
Year 2018
DOI
DOI not found
URL
https://www.pubmed.gov/?cmd=Search&term=Simon%20Armour
Keywords
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

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.