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dc.contributor.advisorClark, Doctor Daniel
dc.contributor.authorSwain, Anthony Jack
dc.date.accessioned2016-11-17T14:22:39Z
dc.date.available2016-11-17T14:22:39Z
dc.date.issued2013-12
dc.identifier.urihttp://hdl.handle.net/10399/3059
dc.description.abstractMultiple target tracking concerns the estimation of an unknown and time-varying number of objects (targets) as they dynamically evolve over time from a sequence of measurements obtained from sensors at discrete time intervals. In the Bayesian ltering framework the estimation problem incorporates natural phenomena such as false measurements and target birth/death. Though theoretically optimal, the generally intractable Bayesian lter requires suitable approximations. This thesis is particularly motivated by a rst-order moment approximation known as the Probability Hypothesis Density (PHD) lter. The emphasis in this thesis is on the further development of the PHD lter for handling more advanced target tracking problems, principally involving multiple group and extended targets. A group target is regarded as a collection of targets that share a common motion or characteristic, while an extended target is regarded as a target that potentially generates multiple measurements. The main contributions are the derivations of the PHD lter for multiple group and extended target tracking problems and their subsequent closed-form solutions. The proposed algorithms are applied in simulated scenarios and their estimate results demonstrate that accurate tracking performance is attainable for certain group/extended target tracking problems. The performance is further analysed with the use of suitable metrics.en_US
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC) Industrial CASE Award Studentshipen_US
dc.language.isoenen_US
dc.publisherHeriot-Watt Universityen_US
dc.publisherEngineering and Physical Sciencesen_US
dc.rightsAll items in ROS are protected by the Creative Commons copyright license (http://creativecommons.org/licenses/by-nc-nd/2.5/scotland/), with some rights reserved.
dc.titleGroup and extended target tracking with the probability hypothesis density filteren_US
dc.typeThesisen_US


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