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

Haque, Fahimul

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

dc.contributor.advisor	Dehghanian, Vahid
dc.contributor.advisor	Fapojuwo, Abraham
dc.contributor.author	Haque, Fahimul
dc.contributor.committeemember	Dehghanian, Vahid
dc.contributor.committeemember	Fapojuwo, Abraham
dc.contributor.committeemember	Nielsen, Jorgen
dc.contributor.committeemember	O'Keefe, Kyle
dc.contributor.committeemember	Messier, Geoffrey
dc.contributor.committeemember	Alves, Paulo
dc.date.accessioned	2024-07-16T16:56:50Z
dc.date.available	2024-07-16T16:56:50Z
dc.date.issued	2024-07-15
dc.description.abstract	With 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.citation	Haque, 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.uri	https://hdl.handle.net/1880/119200
dc.language.iso	en
dc.publisher.faculty	Graduate Studies
dc.publisher.institution	University of Calgary
dc.rights	University 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.subject	GNSS
dc.subject	fault detection and mitigation
dc.subject	integrity monitoring
dc.subject	unsupervised learning
dc.subject	expecation maximization
dc.subject	RAIM
dc.subject.classification	Engineering--Electronics and Electrical
dc.subject.classification	Engineering--Automotive
dc.title	Clustering-Assisted Observation Domain Optimization for GNSS Multi-Fault Detection and Mitigation
dc.type	doctoral thesis
thesis.degree.discipline	Engineering – Electrical & Computer
thesis.degree.grantor	University of Calgary
thesis.degree.name	Doctor of Philosophy (PhD)
ucalgary.thesis.accesssetbystudent	I 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.