{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/10909"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/10909","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"Uncertain input estimation with application to Kalman tracking","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Nashenda, Hubert Tangee"],"institution":"Department of Computer Science","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Mbogho, Audrey J W"],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011","date_published":"2011","updated_at":"2026-07-22T22:23:36Z","subjects":[],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/10909","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Mbogho, Audrey J W"]},{"key":"dc:creator","label":"Author","values":["Nashenda, Hubert Tangee"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2015-01-01T13:11:34Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2015-01-01T13:11:34Z"]},{"key":"dc:date.issued","label":"Date","values":["2011"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Computer Science"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cape Town"]},{"key":"dc:type","label":"Dc Type","values":["Master Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Masters"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["MSc"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11427/10909"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Includes bibliographical references (p. 98-104)."]},{"key":"dc:description.abstract","label":"Abstract","values":["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. 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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."],"dc:identifier.uri":["http://hdl.handle.net/11427/10909"],"dc:language.iso":["eng"],"dc:publisher.department":["Department of Computer Science"],"dc:publisher.institution":["University of Cape Town"],"dc:title":["Uncertain input estimation with application to Kalman tracking"],"dc:type":["Master Thesis"],"dc:type.qualificationlevel":["Masters"],"dc:type.qualificationname":["MSc"]},"updated_at":"2026-07-22T22:23:36Z"}