A New Method to Bound the Integrity Risk for Residual-Based ARAIM

Peng Zhao, Mathieu Joerger, Xiao Liang, Boris Pervan, Yongming Liu

Research output: Contribution to journalArticlepeer-review


This paper develops a tight integrity risk bound for Residual-Based (RB) Advanced Receiver Autonomous Integrity Monitoring (ARAIM). ARAIM measurement models include nominal biases accounting for unknown but bounded errors, and faults of unbounded magnitude. In RB methods, upper bounding the integrity risk requires that one finds the worst-case directions of both the multi-satellite fault vector and of the all-in-view nominal bias vector. Previous methods only account for the worst-case fault direction assuming zero nominal bias. To address this issue, in this paper, we derive a new bounding method in parity space. The method establishes a direct relationship between mean estimation error and RB test statistic non-centrality parameter, which accounts for both faults and nominal errors. ARAIM performance is evaluated to quantify the improvement provided by the proposed method over previous approaches.

Original languageEnglish (US)
JournalIEEE Transactions on Aerospace and Electronic Systems
StateAccepted/In press - 2020


  • Chi-squared
  • Fault Detection and Exclusion
  • GNSS
  • Integrity
  • RAIM

ASJC Scopus subject areas

  • Aerospace Engineering
  • Electrical and Electronic Engineering

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