Aircraft state estimation using cameras and passive radar
| dc.contributor.advisor | Nicolls, Fred | |
| dc.contributor.author | De Charmoy, Benjamin | |
| dc.date.accessioned | 2019-02-18T11:03:33Z | |
| dc.date.available | 2019-02-18T11:03:33Z | |
| dc.date.issued | 2018 | |
| dc.date.updated | 2019-02-18T08:13:26Z | |
| dc.description.abstract | 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. | |
| dc.identifier.apacitation | De Charmoy, B. (2018). <i>Aircraft state estimation using cameras and passive radar</i>. (). University of Cape Town ,Engineering and the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/29613 | en_ZA |
| dc.identifier.chicagocitation | De Charmoy, Benjamin. <i>"Aircraft state estimation using cameras and passive radar."</i> ., University of Cape Town ,Engineering and the Built Environment ,Department of Electrical Engineering, 2018. http://hdl.handle.net/11427/29613 | en_ZA |
| dc.identifier.citation | De Charmoy, B. 2018. Aircraft state estimation using cameras and passive radar. University of Cape Town. | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - De Charmoy, Benjamin AB - 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. DA - 2018 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2018 T1 - Aircraft state estimation using cameras and passive radar TI - Aircraft state estimation using cameras and passive radar UR - http://hdl.handle.net/11427/29613 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/29613 | |
| dc.identifier.vancouvercitation | De Charmoy B. Aircraft state estimation using cameras and passive radar. []. University of Cape Town ,Engineering and the Built Environment ,Department of Electrical Engineering, 2018 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/29613 | en_ZA |
| dc.language.iso | eng | |
| dc.publisher.department | Department of Electrical Engineering | |
| dc.publisher.faculty | Faculty of Engineering and the Built Environment | |
| dc.publisher.institution | University of Cape Town | |
| dc.subject.other | Electrical Engineering | |
| dc.title | Aircraft state estimation using cameras and passive radar | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationname | MSc |