Convolutional neural network based multipath detection method for static and kinematic GPS high precision positioning

Yiming Quan, Lawrence Lau, Gethin Wyn Roberts, Xiaolin Meng, Chao Zhang

Research output: Journal PublicationArticlepeer-review

33 Citations (Scopus)
2 Downloads (Pure)

Abstract

Global Positioning System (GPS) has been used in many aerial and terrestrial high precision positioning applications. Multipath affects positioning and navigation performance. This paper proposes a convolutional neural network based carrier-phase multipath detection method. The method is based on the fact that the features of multipath characteristics in multipath contaminated data can be learned and identified by a convolutional neural network. The proposed method is validated with simulated and real GPS data and compared with existing multipath mitigation methods in position domain. The results show the proposed method can detect about 80% multipath errors (i.e., recall) in both simulated and real data. The impact of the proposed method on positioning accuracy improvement is demonstrated with two datasets, 18–30% improvement is obtained by down-weighting the detected multipath measurements. The focus of this paper is on the development and test of the proposed convolutional neural network based multipath detection algorithm.
Original languageEnglish
Pages (from-to)2052/1-2052/18
JournalRemote Sensing
Volume10
Issue number12
Early online date17 Dec 2018
DOIs
Publication statusPublished Online - 17 Dec 2018

Keywords

  • Convolutional Neural Network (CNN)
  • Global Positioning System (GPS)
  • high precision positioning
  • machine learning
  • multipath detection

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