Sensor Fusion-based Framework for Floor Localization

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dc.contributor.advisorDehghanian, Vahid
dc.contributor.advisorFapojuwo, Abraham Olatunji
dc.contributor.authorHaque, Fahimul
dc.contributor.committeememberNielsen, Jørgen S.
dc.contributor.committeememberMessier, Geoffrey G.
dc.date2018-11
dc.date.accessioned2018-07-05T16:08:45Z
dc.date.available2018-07-05T16:08:45Z
dc.date.issued2018-07-03
dc.description.abstractFloor localization is at the heart of indoor positioning systems (IPSs) in multi-storey buildings with a variety of commercial, industrial, and health and safety applications. The prevalence of wireless technologies along with the integration of micro electro-mechanical sensors (e.g. barometers) in handheld devices and wearable gadgets of current vintage have prompted a surge in research and development efforts in the IPS area. Received signal strength (RSS), barometric altimetry (BA), and differential barometric altimetry (DBA) are three well-known methods of floor localization. However, the RSS-based methods lack the required accuracy, BA-based methods are prone to random errors due to local changes in the air pressure, e.g. from approaching weather systems, and DBA-based methods require installation of additional infrastructure (e.g. reference nodes and ad-hoc network for real-time information exchange). Fusion of BA and RSS is a viable solution for floor localization; nevertheless, available fusion algorithms are rather heuristic. In this dissertation, a theoretical framework is developed for fusing BA and Wi-Fi RSS measurements. The proposed framework involves a novel Monte Carlo Bayesian inference algorithm, for processing RSS measurements, and then fusion with BA using a Kalman Filter scheme. As demonstrated by our experimental results, the proposed sensor fusion algorithm achieves floor localization accuracy of 97% on average. The algorithm does not require new infrastructure, and has low computational complexity, hence, can be readily integrated into various state-of-the-art mobile devices.en_US
dc.identifier.citationHaque, F. (2018). Sensor Fusion-based Framework for Floor Localization (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/32261en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/32261
dc.identifier.urihttp://hdl.handle.net/1880/107039
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.facultySchulich School of Engineering
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
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.subjectIndoor positioning
dc.subjectfloor localization
dc.subjectsensor fusion
dc.subjectRSS
dc.subjectbarometric pressure.
dc.subject.classificationEngineering--Electronics and Electricalen_US
dc.titleSensor Fusion-based Framework for Floor Localization
dc.typemaster thesis
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.item.requestcopytrue
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