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Aircraft state estimation using cameras and passive radar
Multiple target tracking (MTT) is a fundamental task in many application domains. It is a difficult problem to solve in general, so applications make use of domain specific and problem-specific knowledge to approach the problem by solving subtasks separately. This work puts forward a MTT framework (...
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| Format: | Thesis |
| Language: | English |
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Department of Electrical Engineering
2019
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| _version_ | 1867613997294944256 |
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| access_status_str | Open Access |
| author | De Charmoy, Benjamin |
| author2 | Nicolls, Fred |
| author_browse | De Charmoy, Benjamin Nicolls, Fred |
| author_facet | Nicolls, Fred De Charmoy, Benjamin |
| author_sort | De Charmoy, Benjamin |
| collection | Thesis |
| description | Multiple target tracking (MTT) is a fundamental task in many application domains. It is a difficult problem to solve in general, so applications make use of domain specific and problem-specific knowledge to approach the problem by solving subtasks separately. This work puts forward a MTT framework (MTTF) which is based on the Bayesian recursive estimator (BRE). The MTTF extends a particle filter (PF) to handle the multiple targets and adds a probabilistic graphical model (PGM) data association stage to compute the mapping from detections to trackers. The MTTF was applied to the problem of passively monitoring airspace. Two applications were built: a passive radar MTT module and a comprehensive visual object tracking (VOT) system. Both applications require a solution to the MTT problem, for which the MTTF was utilized. The VOT system performed well on real data recorded at the University of Cape Town (UCT) as part of this investigation. The system was able to detect and track aircraft flying within the region of interest (ROI). The VOT system consisted of a single camera, an image processing module, the MTTF module and an evaluation module. The world coordinate frame target localization was within ±3.2 km and these results are presented on Google Earth. The image plane target localization has an average reprojection error of ±17.3 pixels. The VOT system achieved an average area under the curve value of 0.77 for all receiver operating characteristic curves. These performance figures are typical over the ±1 hr of video recordings taken from the UCT site. The passive radar application was tested on simulated data. The MTTF module was designed to connect to an existing passive radar system developed by Peralex Electronics Pty Ltd. The MTTF module estimated the number of targets in the scene and localized them within a 2D local world Cartesian coordinate system. The investigations encompass numerous areas of research as well as practical aspects of software engineering and systems design. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/29613 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:45:02.180Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | Department of Electrical Engineering |
| publisherStr | Department of Electrical Engineering |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/29613 Aircraft state estimation using cameras and passive radar De Charmoy, Benjamin Nicolls, Fred Electrical Engineering Multiple target tracking (MTT) is a fundamental task in many application domains. It is a difficult problem to solve in general, so applications make use of domain specific and problem-specific knowledge to approach the problem by solving subtasks separately. This work puts forward a MTT framework (MTTF) which is based on the Bayesian recursive estimator (BRE). The MTTF extends a particle filter (PF) to handle the multiple targets and adds a probabilistic graphical model (PGM) data association stage to compute the mapping from detections to trackers. The MTTF was applied to the problem of passively monitoring airspace. Two applications were built: a passive radar MTT module and a comprehensive visual object tracking (VOT) system. Both applications require a solution to the MTT problem, for which the MTTF was utilized. The VOT system performed well on real data recorded at the University of Cape Town (UCT) as part of this investigation. The system was able to detect and track aircraft flying within the region of interest (ROI). The VOT system consisted of a single camera, an image processing module, the MTTF module and an evaluation module. The world coordinate frame target localization was within ±3.2 km and these results are presented on Google Earth. The image plane target localization has an average reprojection error of ±17.3 pixels. The VOT system achieved an average area under the curve value of 0.77 for all receiver operating characteristic curves. These performance figures are typical over the ±1 hr of video recordings taken from the UCT site. The passive radar application was tested on simulated data. The MTTF module was designed to connect to an existing passive radar system developed by Peralex Electronics Pty Ltd. The MTTF module estimated the number of targets in the scene and localized them within a 2D local world Cartesian coordinate system. The investigations encompass numerous areas of research as well as practical aspects of software engineering and systems design. 2019-02-18T11:03:33Z 2019-02-18T11:03:33Z 2018 2019-02-18T08:13:26Z Master Thesis Masters MSc http://hdl.handle.net/11427/29613 eng application/pdf Department of Electrical Engineering Faculty of Engineering and the Built Environment University of Cape Town |
| spellingShingle | Electrical Engineering De Charmoy, Benjamin Aircraft state estimation using cameras and passive radar |
| thesis_degree_str | Master's |
| title | Aircraft state estimation using cameras and passive radar |
| title_full | Aircraft state estimation using cameras and passive radar |
| title_fullStr | Aircraft state estimation using cameras and passive radar |
| title_full_unstemmed | Aircraft state estimation using cameras and passive radar |
| title_short | Aircraft state estimation using cameras and passive radar |
| title_sort | aircraft state estimation using cameras and passive radar |
| topic | Electrical Engineering |
| url | http://hdl.handle.net/11427/29613 |
| work_keys_str_mv | AT decharmoybenjamin aircraftstateestimationusingcamerasandpassiveradar |