Uncertain input estimation with application to Kalman tracking

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dc.contributor.advisorMbogho, Audrey J Wen_ZA
dc.contributor.authorNashenda, Hubert Tangeeen_ZA
dc.date.accessioned2015-01-01T13:11:34Z
dc.date.available2015-01-01T13:11:34Z
dc.date.issued2011en_ZA
dc.descriptionIncludes bibliographical references (p. 98-104).en_ZA
dc.description.abstractMany motion tracking systems average and integrate tracking measurements over a period of time in order to reduce the effects of device noise, external noise and other disturbances. The target (user) is likely to be moving throughout the sample time, introducing additional 'noise' (uncertainty) into the measurements. Without filtering, noise can cause small variations in the estimated tracking positions (tracking drift) over time. There are many filters and algorithms that account for uncertainty due to noise. The Kalman filter has been chosen in this study because of its ability to estimate tracking positions and to account for uncertainty in the tracked object's position where it is occluded by other stationary or moving objects. An inexpensive algorithm is presented which detects the slightest motion and then tracks the motion or the target very accurately.en_ZA
dc.identifier.apacitationNashenda, H. T. (2011). <i>Uncertain input estimation with application to Kalman tracking</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Computer Science. Retrieved from http://hdl.handle.net/11427/10909en_ZA
dc.identifier.chicagocitationNashenda, Hubert Tangee. <i>"Uncertain input estimation with application to Kalman tracking."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Computer Science, 2011. http://hdl.handle.net/11427/10909en_ZA
dc.identifier.citationNashenda, H. 2011. Uncertain input estimation with application to Kalman tracking. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Nashenda, Hubert Tangee AB - Many motion tracking systems average and integrate tracking measurements over a period of time in order to reduce the effects of device noise, external noise and other disturbances. The target (user) is likely to be moving throughout the sample time, introducing additional 'noise' (uncertainty) into the measurements. Without filtering, noise can cause small variations in the estimated tracking positions (tracking drift) over time. There are many filters and algorithms that account for uncertainty due to noise. The Kalman filter has been chosen in this study because of its ability to estimate tracking positions and to account for uncertainty in the tracked object's position where it is occluded by other stationary or moving objects. An inexpensive algorithm is presented which detects the slightest motion and then tracks the motion or the target very accurately. DA - 2011 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2011 T1 - Uncertain input estimation with application to Kalman tracking TI - Uncertain input estimation with application to Kalman tracking UR - http://hdl.handle.net/11427/10909 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/10909
dc.identifier.vancouvercitationNashenda HT. Uncertain input estimation with application to Kalman tracking. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Computer Science, 2011 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/10909en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Computer Scienceen_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherInformation Technologyen_ZA
dc.titleUncertain input estimation with application to Kalman trackingen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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