Machine Learning for Robust Detection and Mitigation of GNSS Multipath

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dc.contributor.advisorO'Keefe, Kyle
dc.contributor.advisorBroumandan, Ali
dc.contributor.authorPhillips, Christian Michael
dc.contributor.committeememberBayat, Sayeh
dc.contributor.committeememberEl-Sheimy, Naser
dc.date2025-06
dc.date.accessioned2025-01-07T22:54:28Z
dc.date.available2025-01-07T22:54:28Z
dc.date.issued2025-01-07
dc.description.abstractDistortion to the correlation function caused by multipath and non-line of sight signals can result in pseudorange errors on the order of several tens of meters in urban canyon environments. To address this problem, a deep learning approach for classifying multipath and estimating observation weights from a global navigation satellite systems (GNSS) receiver correlation function is presented. This approach uses a 1-dimensional convolutional neural network, suitable for embedded applications, to classify the magnitude of pseudorange error associated with correlation functions and to generate a multipath probability used for deriving observation weights. The network is trained and tested on live GNSS data collected in a challenging urban environment. The capability of the model to remove high error measurements for a least-squares positioning solution and to generate weights for a weighted least-squares positioning solution is explored. The network has proven to be effective at detecting measurements with high multipath ranging error, and to effectively generalize to unseen data. The removal of detected measurements reduced positioning error by up to 80%, and the use of deep learning derived weights in the positioning solution reduced positioning error by a further 50%. In both cases, positioning errors were comparable to what is expected for an open-sky single frequency standalone positioning solution.
dc.identifier.citationPhillips, C. M. (2025). Machine learning for robust detection and mitigation of GNSS multipath (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.urihttps://hdl.handle.net/1880/120384
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.subjectMachine Learning
dc.subjectDeep Learning
dc.subjectMultipath
dc.subjectPositioning
dc.subjectNavigation
dc.subject.classificationEngineering
dc.titleMachine Learning for Robust Detection and Mitigation of GNSS Multipath
dc.typemaster thesis
thesis.degree.disciplineEngineering – Geomatics
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.thesis.accesssetbystudentI do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.
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