Enhancement of Localization Systems in NLOS Urban Scenario with Multipath Ray Tracing Fingerprints and Machine Learning
Current Language EN
English English Türkçe Türkçe Español Español Português Português Pусский Pусский Filipino Filipino Tiếng Việt Tiếng Việt हिन्दी हिन्दी العربية العربية বাংলা বাংলা Deutsch Deutsch 中文 中文

Enhancement of Localization Systems in NLOS Urban Scenario with Multipath Ray Tracing Fingerprints and Machine Learning

N de Sousa M;S Thomä R;;
Sensors (Basel, Switzerland) 2018 Vol. 18 pp. -
147
m2018sensorsenhancement

Abstract

A hybrid technique is proposed to enhance the localization performance of a time difference of arrival (TDOA) deployed in non-line-of-sight (NLOS) suburban scenario. The idea was to use Machine Learning framework on the dataset, produced by the ray tracing simulation, and the Channel Impulse Respons …

Keywords

National Center for Biotechnology Information NCBI NLM MEDLINE pubmed abstract nih national institutes of health national library of medicine pmid:30469418 pmc6263810 doi:10.3390/s18114073 marcelo n de sousa reiner s thomä

Access

URL:
https://www.pubmed.gov/?cmd=Search&term=Reiner%20S...

Citation

ID: 267015
Ref Key: m2018sensorsenhancement
Use this key to autocite in SciMatic or Thesis Manager

References

References

No Bibliography

Blockchain Verification

Account:
NFT Contract Address:
0x95644003c57E6F55A65596E3D9Eac6813e3566dA
Article ID:
267015
Unique Identifier:
Network:
Scimatic Chain (ID: 481)
Blockchain Readiness Checklist
Authors
Abstract
Journal Name
Year
Title
5/5
Creates 1,000,000 NFT tokens for this article
Token Features:
  • ERC-1155 Standard NFT
  • 1 Million Supply per Article
  • Transferable via MetaMask
  • Permanent Blockchain Record
Blockchain QR Code
Scan with Saymatik Web3.0 Wallet
Gas fees required in SCI Coins
Buy SCI

Saymatik Web3.0 Wallet

Google Play
App Store