Monitoring and Mitigation of Ionospheric Anomalies for GNSS-Based Safety Critical Systems: A review of up-to-date signal processing techniques

Jiyun Lee, Y. T.Jade Morton, Jinsil Lee, Hee Seung Moon, Jiwon Seo

Research output: Contribution to journalArticlepeer-review

61 Citations (Scopus)

Abstract

The ionosphere has been the most challenging source of error to mitigate within the community of global navigation satellite system (GNSS)-based safety-critical systems. Users of those systems should be assured that the difference between an unknown true position and a system-derived position estimate is bounded with an extremely high degree of confidence. One of the major concerns for meeting this requirement, known as integrity, is ionosphere-induced error or discontinuity of GNSS signals significant enough to threaten the safety of users. The potentially hazardous ionospheric anomalies of interest in this article are ionospheric spatial decorrelation and ionospheric scintillation under disturbed conditions. As the demand of safety-critical navigation applications increases with the rapid growth of the autonomous vehicle sector, ionospheric monitoring and mitigation techniques become more important to support such systems.

Original languageEnglish
Article number8026593
Pages (from-to)96-110
Number of pages15
JournalIEEE Signal Processing Magazine
Volume34
Issue number5
DOIs
Publication statusPublished - 2017 Sep

Bibliographical note

Funding Information:
Jiyun Lee was supported by the Space Core Technology Development Program of the National Research Foundation (NRF) funded by the Ministry of Science, ICT, and Future Planning (NRF-2014M1A3A3A02034937). Hee-Seung Moon and Jiwon Seo were supported by the Ministry of Science, ICT, and Future Planning (MSIP), South Korea, under the “ICT Consilience Creative Program” (IITP-2017-2017-0-01015) supervised by the Institute for Information and Communications Technology Promotion. Jiwon Seo is the corresponding author of this article.

Publisher Copyright:
© 2017 IEEE.

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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