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RAIM algorithm based on fuzzy clustering analysis

  • Shouzhou Gu
  • , Jinzhong Bei*
  • , Chuang Shi
  • , Yaming Dang
  • , Zuoya Zheng
  • , Congcong Cui
  • *Corresponding author for this work
  • Chinese Academy of Surveying and Mapping
  • Wuhan University
  • China Academy of Electronics and Information Technology
  • Beijing Xinxing Huaan Intelligence Technology Co. Ltd.

Research output: Contribution to journalArticlepeer-review

Abstract

With the development of various navigation systems (such as GLONASS, Galileo, BDS), there is a sharp increase in the number of visible satellites. Accordingly, the probability of multiply gross measurements will increase. However, the conventional RAIM methods are difficult to meet the demands of the navigation system. In order to solve the problem of checking and identify multiple gross errors of receiver autonomous integrity monitoring (RAIM), this paper designed full matrix of single point positioning by QR decomposition, and proposed a new RAIM algorithm based on fuzzy clustering analysis with fuzzy c-means (FCM). And on the condition of single or two gross errors, the performance of hard or fuzzy clustering analysis were compared. As the results of the experiments, the fuzzy clustering method based on FCM principle could detect multiple gross error effectively, also achieved the quality control of single point positioning and ensured better reliability results.

Original languageEnglish
Pages (from-to)281-293
Number of pages13
JournalCMES - Computer Modeling in Engineering and Sciences
Volume119
Issue number2
DOIs
StatePublished - 2019
Externally publishedYes

Keywords

  • FCM
  • Integrity
  • RAIM
  • Single point positioning

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