GNSS multipath detection using a machine learning approach

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

45 Citations (Scopus)


Insufficient localization accuracy of global navigation satellite system (GNSS) receivers is one of the challenges to implement advanced intelligent transportation system in highly urbanized areas. Multipath and non-line-of-sight (NLOS) effects strongly deteriorate GNSS positioning performance. This paper aims to train a classifier by supervised machine learning to separate the type of GNSS pseudorange measurement into three categories, clean, multipath and NLOS. Several features obtained or calculated from the GNSS raw data are evaluated. This paper also proposes a new feature to indicate the consistency between measurements of pseudorange and Doppler shift. According to the experiment result, about 75% of classification accuracy can be achieved using a support vector machine (SVM) classifier trained by the proposed feature and received signal strength.

Original languageEnglish
Title of host publication2017 IEEE 20th International Conference on Intelligent Transportation Systems, ITSC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538615256
Publication statusPublished - 14 Mar 2018
Event20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017 - Yokohama, Kanagawa, Japan
Duration: 16 Oct 201719 Oct 2017

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC


Conference20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017
CityYokohama, Kanagawa


  • Global Positioning System
  • Machine Learning
  • Multipath
  • NLOS
  • Support Vector Machine
  • Urban Area

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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