Clustering-Assisted Observation Domain Optimization for GNSS Multi-Fault Detection and Mitigation

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dc.contributor.advisorDehghanian, Vahid
dc.contributor.advisorFapojuwo, Abraham
dc.contributor.authorHaque, Fahimul
dc.contributor.committeememberDehghanian, Vahid
dc.contributor.committeememberFapojuwo, Abraham
dc.contributor.committeememberNielsen, Jorgen
dc.contributor.committeememberO'Keefe, Kyle
dc.contributor.committeememberMessier, Geoffrey
dc.contributor.committeememberAlves, Paulo
dc.date.accessioned2024-07-16T16:56:50Z
dc.date.available2024-07-16T16:56:50Z
dc.date.issued2024-07-15
dc.description.abstractWith the rise of autonomous and semi-autonomous vehicles, effective fault detection and mitigation (FDM) methods have become essential in meeting the integrity requirements for precise and reliable Global Navigation Satellite System (GNSS)-based positioning. In scenarios involving multiple faulty observations, the existing GNSS-only statistical FDM methods are ineffective or impractical due to either theoretical model limitations or high computational costs. Additionally, supervised learning-based FDM approaches introduced in recent years do not meet the existing and emerging industry requirements due to dependence on large amounts of diverse training data, the accuracy of the offline labeling process, or high computational complexity. In this dissertation, a novel GNSS multi-fault detection and mitigation method is developed that achieves a balance between computational complexity and performance. The proposed method incorporates an Expectation Maximization (EM) framework to jointly estimate an approximate maximum likelihood of states and latent model parameters in the presence of observation outliers, i.e., faults. However, the EM algorithm is known for its high computational complexity. To reduce the computational complexity of EM, an importance sampling step based on unsupervised clustering is introduced. As demonstrated by the results and analysis herein, the proposed method outperforms the existing Least-squares Residuals-based single-fault method, achieving an average improvement of up to 48% in positioning accuracy. Additionally, the computational complexity of the proposed method is an order of magnitude lower than the state-of-the-art Solution Separation method. The improved performance and the lower computational complexity of the proposed method make it a suitable candidate for integration into modern standalone real-time GNSS applications.
dc.identifier.citationHaque, F. (2024). Clustering-assisted observation domain optimization for GNSS multi-fault detection and mitigation (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.urihttps://hdl.handle.net/1880/119200
dc.identifier.urihttps://doi.org/10.11575/PRISM/46796
dc.language.isoen
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgary
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectGNSS
dc.subjectfault detection and mitigation
dc.subjectintegrity monitoring
dc.subjectunsupervised learning
dc.subjectexpecation maximization
dc.subjectRAIM
dc.subject.classificationEngineering--Electronics and Electrical
dc.subject.classificationEngineering--Automotive
dc.titleClustering-Assisted Observation Domain Optimization for GNSS Multi-Fault Detection and Mitigation
dc.typedoctoral thesis
thesis.degree.disciplineEngineering – Electrical & Computer
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameDoctor of Philosophy (PhD)
ucalgary.thesis.accesssetbystudentI require a thesis withhold – I need to delay the release of my thesis due to a patent application, and other reasons outlined in the link above. I have/will need to submit a thesis withhold application.
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