DOA-based localization algorithms under NLOS conditions

Jun Li, I. Tai Lu, Jonathan S. Lu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Localization schemes based on direction of arrival (DOA) in none-line-of-sight (NLOS) environments are developed. The proposed kernel-based machine learning method is innovative and can provide accurate position estimation under none-line-of sight (NLOS) conditions. The proposed kernel-based method is compared with the Weighted K-nearest neighborhood (WKNN) fingerprinting method using simulated DOA data in practical rural environment. It shows that the kernel-based method gives more accurate localization results.

Original languageEnglish (US)
Title of host publication2018 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538650295
DOIs
StatePublished - Jun 8 2018
Event2018 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2018 - Farmingdale, United States
Duration: May 4 2018 → …

Publication series

Name2018 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2018

Other

Other2018 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2018
CountryUnited States
CityFarmingdale
Period5/4/18 → …

Keywords

  • AOA
  • DOA
  • Kernel-based Machine Learning
  • Localization
  • NLOS
  • fingerprinting

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications
  • Information Systems and Management
  • Education
  • Health(social science)

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