Intelligent GNSS Satellite Visibility Classification in Urban Areas: A Deep Learning Approach with Interpretation

Zekun Zhang, Penghui Xu, Guohao Zhang, Li Ta Hsu

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

Abstract

Accurate and reliable GNSS solutions are essential for the development of intelligent transportation systems. However, GNSS signals can be easily blocked by buildings in urban areas, resulting in the sole reception of the reflected signal with large errors, namely non-line-of-sight (NLOS) receptions. Thus, it is necessary to classify the visible satellite measurements from NLOS receptions before conducting positioning. This paper aims to design a Transformer-based deep learning network to utilize the spatial correlations between satellites for their visibility classification. The proposed method achieves about 89 percent classification accuracy in validation and test data. By exploring the spatial correlation between satellites in the attention matrix of Transformer, we reveal the mechanism of deep learning network on satellite visibility classification.

Original languageEnglish
Title of host publication2023 IEEE 26th International Conference on Intelligent Transportation Systems, ITSC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5969-5975
Number of pages7
ISBN (Electronic)9798350399462
DOIs
Publication statusPublished - 2023
Event26th IEEE International Conference on Intelligent Transportation Systems, ITSC 2023 - Bilbao, Spain
Duration: 24 Sept 202328 Sept 2023

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
ISSN (Print)2153-0009
ISSN (Electronic)2153-0017

Conference

Conference26th IEEE International Conference on Intelligent Transportation Systems, ITSC 2023
Country/TerritorySpain
CityBilbao
Period24/09/2328/09/23

Keywords

  • Attention Mechanism
  • Deep Learning
  • GNSS
  • NLOS
  • Transformer

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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