Kalman Filter Based Sensor Placement For Burgers Equation

dc.contributor.authorZonov, Stanislav
dc.date.accessioned2019-06-03T13:01:34Z
dc.date.available2019-06-03T13:01:34Z
dc.date.issued2019-06-03
dc.date.submitted2019-05-20
dc.description.abstractThe algorithm used for determining sensor placement in this thesis will be based on the Kalman filter. This filter is very famous and its application are numerous - some examples include aircraft navigation, finance and weather forecasting. It is used to extract an estimate of the true state of a system based on noisy measurements. For linear systems, where the noise satisfies certain assumptions, the Kalman filter minimizes the expected squared error between the filter's estimate and the true state of the system. By varying the sensor location, and therefore the observation matrix, one can further minimize the expected squared error to determine an optimal sensor placement. For linear systems, a regular as well as steady state Kalman filter are used for the sensor placement algorithm. This thesis examines this concept further, for nonlinear systems by using the Extended Kalman filter.en
dc.identifier.urihttp://hdl.handle.net/10012/14740
dc.language.isoenen
dc.pendingfalse
dc.publisherUniversity of Waterlooen
dc.titleKalman Filter Based Sensor Placement For Burgers Equationen
dc.typeMaster Thesisen
uws-etd.degreeMaster of Mathematicsen
uws-etd.degree.departmentApplied Mathematicsen
uws-etd.degree.disciplineApplied Mathematicsen
uws-etd.degree.grantorUniversity of Waterlooen
uws.contributor.advisorStastna, Marek
uws.contributor.advisorMorris, Kirsten
uws.contributor.affiliation1Faculty of Mathematicsen
uws.peerReviewStatusUnrevieweden
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws.scholarLevelGraduateen
uws.typeOfResourceTexten

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