Bayesian fault detection, identification, and adaptation for GNSS applications
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...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
IEEE
2024
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/95881 |
| _version_ | 1848766054527926272 |
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| author | Yu, Yangkang Yang, Ling Shen, Yunzhong El-Mowafy, Ahmed |
| author_facet | Yu, Yangkang Yang, Ling Shen, Yunzhong El-Mowafy, Ahmed |
| author_sort | Yu, Yangkang |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:45:03Z |
| format | Journal Article |
| id | curtin-20.500.11937-95881 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:45:03Z |
| publishDate | 2024 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-958812024-11-13T07:01:11Z Bayesian fault detection, identification, and adaptation for GNSS applications Yu, Yangkang Yang, Ling Shen, Yunzhong El-Mowafy, Ahmed Bayesian estimation Fault detection GNSS GPS Integrity Monitoring 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. 2024 Journal Article http://hdl.handle.net/20.500.11937/95881 10.1109/TAES.2024.3456757 IEEE restricted |
| spellingShingle | Bayesian estimation Fault detection GNSS GPS Integrity Monitoring Yu, Yangkang Yang, Ling Shen, Yunzhong El-Mowafy, Ahmed Bayesian fault detection, identification, and adaptation for GNSS applications |
| title | Bayesian fault detection, identification, and adaptation for GNSS applications |
| title_full | Bayesian fault detection, identification, and adaptation for GNSS applications |
| title_fullStr | Bayesian fault detection, identification, and adaptation for GNSS applications |
| title_full_unstemmed | Bayesian fault detection, identification, and adaptation for GNSS applications |
| title_short | Bayesian fault detection, identification, and adaptation for GNSS applications |
| title_sort | bayesian fault detection, identification, and adaptation for gnss applications |
| topic | Bayesian estimation Fault detection GNSS GPS Integrity Monitoring |
| url | http://hdl.handle.net/20.500.11937/95881 |