Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Bayesian fault detection, identification, and adaptation for GNSS applications

    Access Status
    Fulltext not available
    Authors
    Yu, Yangkang
    Yang, Ling
    Shen, Yunzhong
    El-Mowafy, Ahmed
    Date
    2024
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Yu, Y. and Yang, L. and Shen, Y. and El-Mowafy, A. 2024. Bayesian fault detection, identification, and adaptation for GNSS applications. IEEE Transactions on Aerospace and Electronic Systems.
    Source Title
    IEEE Transactions on Aerospace and Electronic Systems
    DOI
    10.1109/TAES.2024.3456757
    ISSN
    0018-9251
    Faculty
    Faculty of Science and Engineering
    School
    School of Earth and Planetary Sciences (EPS)
    URI
    http://hdl.handle.net/20.500.11937/95881
    Collection
    • Curtin Research Publications
    Abstract

    This contribution introduces a Bayesian framework of fault detection, identification, and adaptation (Bayesian DIA) methods for Global Navigation Satellite System (GNSS) applications. It provides an alternative to the classical DIA approach, which allows for leveraging the prior information about faults to enhance the robustness of DIA estimators and subsequently use posterior information to implement quality control. In this framework, the Bernoulli-Gaussian (BG) model is first used to construct the prior distribution of faults describing prior information about the mode and size of faults. Next, a DIA method based on Bayesian hypotheses testing (DIA-BHT) is proposed to process the additive faults in linear observation systems. Finally, the Bayesian DIA probability and credibility levels are introduced as measures for quality control. These probability levels describe the probabilities of decisions conditioned on the realities, which enables the prediction of the possibility of making a correct decision. The credibility levels denote the probabilities of realities conditioned on the decisions, which is helpful for the assessment of decision correctness. GNSS examples verified that the proposed Bayesian DIA method is robust for detecting and identifying faults with different modes and sizes.

    Related items

    Showing items related by title, author, creator and subject.

    • The detection of multiple faults in a Bayesian setting using dynamic programming approaches
      Habibi, Hamed ; Howard, Ian ; Habibi, R. (2020)
      ©2020 Elsevier B.V. Inspired by the need for improving the reliability and safety of complex dynamic systems, this paper tackles the multiple faults detection problem using Dynamic Programming (DP) based methods under the ...
    • Bayesian Sensor Fault Detection in a Markov Jump System
      Habibi, H.; Howard, Ian ; Habibi, R. (2017)
      © 2017 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd.In this paper, the fault detection of a latent fault in a sensor for a Markov jump system is studied. It is equivalent to detecting a change ...
    • Bayesian Fault Probability Estimation: Application in Wind Turbine Drivetrain Sensor Fault Detection
      Habibi, Hamed; Howard, Ian ; Habibi, R. (2018)
      In this paper, the extension of the Bayesian framework for sensor fault detection of nonlinear systems proposed in [25] is studied utilizing particle filtering and the expectation maximization (EM) algorithm, in which the ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.