S-PDR: A Novel Pedestrian Dead Reckoning Algorithm with step-based attitude corrections for Free-Moving Handheld devices

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dc.contributor.advisorEl-Sheimy, Naser
dc.contributor.authorkhedr, maan E.
dc.contributor.committeememberNoureldin, Aboelmagd
dc.contributor.committeememberGao, Yang
dc.contributor.committeememberO'Keefe, Kyle
dc.contributor.committeememberFapojuwo, Abraham O.
dc.contributor.committeememberChen, Ruizhi
dc.dateFall Convocation
dc.date.accessioned2021-08-04T22:24:38Z
dc.date.embargolift2021-07-08
dc.date.issued2021-01-08
dc.description.abstractMobile location-based services (MLBS) are attracting attention for their potential public applications and personal use. MLBS can be used for a variety of applications such as location-based advertisement, smart shopping, smart cities, health applications, emergency response, and even gamming. The majority of these applications are used in indoor environments where the well established GNSS navigation solutions are hindered or even unavailable and hence they rely on alternative navigation solutions such Inertial Navigation Systems (INS). To date, the main challenges for MLBS is to provide accurate and reliable navigation solution under varying circumstances such as indoor or outdoor, while reducing system cost and having real-time applicability, which is achieved through the use of MEMS technology. However, MEMS sensors suffer from high errors and noise to signal ratio that results in quick divergence of the INS solution, hence the need for aiding. This thesis aims at providing a Pedestrian Dead Reckoning (PDR) solution that uses off-the-shelf sensors in mobile devices to provide short term reliable navigation solution that helps reduce the complexity and frequency of relying on aiding techniques through developing a novel PDR system S-PDR . S-PDR utilizes a novel step detection technique that is motion-mode and use-case invariant, an attitude correction technique that can provide corrections as frequently as a step-by-step basis, and an enhanced PCA-based heading estimation. Testing results in comparison to XSense MTi G-710 which is a high-end MEMS sensor show that S-PDR provide reliable short-term navigation solution with final positioning error that is up to 6 meters after 3 minutes operation time, outperforming the on-board fusion solution provided by the XSense. The short term enhancement of the PDR solution reliability can help reduce the operational complexity of aiding navigation systems such as RF-based indoor navigation and Magnetic Map Matching as it reduces the frequency by which these aiding techniques are required and applied.
dc.identifier.citationKhedr, M. E. (2020). S-PDR: A Novel Pedestrian Dead Reckoning Algorithm with step-based attitude corrections for Free-Moving Handheld devices (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/39070
dc.identifier.urihttp://hdl.handle.net/1880/113704
dc.language.isoenen
dc.language.isoEnglish
dc.publisher.facultyGraduate Studiesen
dc.publisher.facultySchulich School of Engineering
dc.publisher.institutionUniversity of Calgaryen
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.en
dc.subjectPedestrian Dead Reckoning
dc.subjectAttitude correction
dc.subjectStep detection
dc.subjectHeading Estimation
dc.subjectLeast-Squares Estimation
dc.subjectComplementary Filter
dc.subjectSensor fusion
dc.subjectSmartphone
dc.subject.classificationEngineering--General
dc.titleS-PDR: A Novel Pedestrian Dead Reckoning Algorithm with step-based attitude corrections for Free-Moving Handheld devices
dc.typedoctoral thesis
thesis.degree.disciplineEngineering Geomatics
thesis.degree.grantorUniversity of Calgaryen
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameDoctor of Philosophy (PhD)
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