Nlos multipath classification of gnss signal correlation output using machine learning

Taro Suzuki*, Yoshiharu Amano

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)


This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.

Original languageEnglish
Article number2503
Issue number7
Publication statusPublished - 2021 Apr 1


  • GNSS
  • GPS
  • Machine learning
  • Multipath
  • SDR
  • Signal classification

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering


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