Uncertain input estimation with application to Kalman tracking

 

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dc.contributor.advisor Mbogho, Audrey J W en_ZA
dc.contributor.author Nashenda, Hubert Tangee en_ZA
dc.date.accessioned 2015-01-01T13:11:34Z
dc.date.available 2015-01-01T13:11:34Z
dc.date.issued 2011 en_ZA
dc.identifier.citation Nashenda, H. 2011. Uncertain input estimation with application to Kalman tracking. University of Cape Town. en_ZA
dc.identifier.uri http://hdl.handle.net/11427/10909
dc.description Includes bibliographical references (p. 98-104). en_ZA
dc.description.abstract 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. en_ZA
dc.language.iso eng en_ZA
dc.subject.other Information Technology en_ZA
dc.title Uncertain input estimation with application to Kalman tracking en_ZA
dc.type Thesis / Dissertation en_ZA
uct.type.publication Research en_ZA
uct.type.resource Thesis en_ZA
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Science en_ZA
dc.publisher.department Department of Computer Science en_ZA
dc.type.qualificationlevel Masters en_ZA
dc.type.qualificationname MSc en_ZA
uct.type.filetype Text
uct.type.filetype Image


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