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Uncertain input estimation with application to Kalman tracking
Includes bibliographical references (p. 98-104).
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| Main Author: | |
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
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Department of Computer Science
2015
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| _version_ | 1867613861511692288 |
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| access_status_str | Open Access |
| author | Nashenda, Hubert Tangee |
| author2 | Mbogho, Audrey J W |
| author_browse | Mbogho, Audrey J W Nashenda, Hubert Tangee |
| author_facet | Mbogho, Audrey J W Nashenda, Hubert Tangee |
| author_sort | Nashenda, Hubert Tangee |
| collection | Thesis |
| description | Includes bibliographical references (p. 98-104). |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/10909 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:42:52.688Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2015 |
| publishDateRange | 2015 |
| publishDateSort | 2015 |
| publisher | Department of Computer Science |
| publisherStr | Department of Computer Science |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/10909 Uncertain input estimation with application to Kalman tracking Nashenda, Hubert Tangee Mbogho, Audrey J W Information Technology Includes bibliographical references (p. 98-104). 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. 2015-01-01T13:11:34Z 2015-01-01T13:11:34Z 2011 Master Thesis Masters MSc http://hdl.handle.net/11427/10909 eng application/pdf Department of Computer Science Faculty of Science University of Cape Town |
| spellingShingle | Information Technology Nashenda, Hubert Tangee Uncertain input estimation with application to Kalman tracking |
| thesis_degree_str | Master's |
| title | Uncertain input estimation with application to Kalman tracking |
| title_full | Uncertain input estimation with application to Kalman tracking |
| title_fullStr | Uncertain input estimation with application to Kalman tracking |
| title_full_unstemmed | Uncertain input estimation with application to Kalman tracking |
| title_short | Uncertain input estimation with application to Kalman tracking |
| title_sort | uncertain input estimation with application to kalman tracking |
| topic | Information Technology |
| url | http://hdl.handle.net/11427/10909 |
| work_keys_str_mv | AT nashendahuberttangee uncertaininputestimationwithapplicationtokalmantracking |