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dc.contributor.advisorChakravorty , Suman
dc.creatorFaber, Weston Ryan
dc.date.accessioned2019-01-17T17:13:06Z
dc.date.available2019-01-17T17:13:06Z
dc.date.created2018-05
dc.date.issued2018-05-08
dc.date.submittedMay 2018
dc.identifier.urihttp://hdl.handle.net/1969.1/173387
dc.description.abstractIn order to protect assets and operations in space, it is critical to collect and maintain accurate information regarding Resident Space Objects (RSOs). This collection of information is typically known as Space Situational Awareness (SSA). Ground-based and space-based sensors provide information regarding the RSOs in the form of observations or measurement returns. However, the distance between RSO and sensor can, at times, be tens of thousands of kilometers. This and other factors lead to noisy measurements that, in turn, cause one to be uncertain about which RSO a measurement belongs to. These ambiguities are known as data association ambiguities. Coupled with uncertainty in RSO state and the vast number of objects in space, data association ambiguities can cause the multiple space object-tracking problem to become computationally intractable. Tracking the RSO can be framed as a recursive Bayesian multiple object tracking problem with state space containing both continuous and discrete random variables. Using a Finite Set Statistics (FISST) approach one can derive the Random Finite Set (RFS) based Bayesian multiple object tracking recursions. These equations, known as the FISST multiple object tracking equations, are computationally intractable when solved in full. This computational intractability provokes the idea of the newly developed alternative hypothesis dependent derivation of the FISST equations. This alternative derivation allows for a Markov Chain Monte Carlo (MCMC) based randomized sampling technique, termed Randomized FISST (R-FISST). R-FISST is found to provide an accurate approximation of the full FISST recursions while keeping the problem tractable. There are many other benefits to this new derivation. For example, it can be used to connect and compare the classical tracking methods to the modern FISST based approaches. This connection clearly defines the relationships between different approaches and shows that they result in the same formulation for scenarios with a fixed number of objects and are very similar in cases with a varying number of objects. Findings also show that the R-FISST technique is compatible with many powerful optimization tools and can be scaled to solve problems such as collisional cascading.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectSpace Situational Awarenessen
dc.subjectMultiple Object Trackingen
dc.subjectRandomized Hypothesis Generationen
dc.subjectRandom Finite Setsen
dc.subjectMultiple Hypothesis Trackingen
dc.subjectCollisional Cascadingen
dc.subjectSpace Object Fragmentationen
dc.subjectMarkov Chain Monte Carloen
dc.titleMultiple Space Object Tracking Using A Randomized Hypothesis Generation Techniqueen
dc.typeThesisen
thesis.degree.departmentAerospace Engineeringen
thesis.degree.disciplineAerospace Engineeringen
thesis.degree.grantorTexas A & M Universityen
thesis.degree.nameDoctor of Philosophyen
thesis.degree.levelDoctoralen
dc.contributor.committeeMemberVadali, Srinivas R
dc.contributor.committeeMemberBhattacharya, Raktim
dc.contributor.committeeMemberKumar, P R
dc.type.materialtexten
dc.date.updated2019-01-17T17:13:06Z
local.etdauthor.orcid0000-0003-4830-1614


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