Title:
Initialization of sequential estimation for unobservable dynamical systems using partial information in the presence of systemic uncertainty

dc.contributor.advisor Holzinger, Marcus J.
dc.contributor.author Worthy, Johnny Lee
dc.contributor.committeeMember Lightsey, Glenn
dc.contributor.committeeMember Costello, Mark F.
dc.contributor.committeeMember Scheeres, Daniel
dc.contributor.committeeMember Blake, Travis
dc.contributor.department Aerospace Engineering
dc.date.accessioned 2017-06-07T17:47:31Z
dc.date.available 2017-06-07T17:47:31Z
dc.date.created 2017-05
dc.date.issued 2017-04-07
dc.date.submitted May 2017
dc.date.updated 2017-06-07T17:47:31Z
dc.description.abstract Space Situational Awareness (SSA) is defined the ability to characterize as fully as possible the space environment. Short, unobservable measurement sequences pose a challenge for traditional state estimation methodologies and instead admissible region based methods are used. The primary question addressed in this work is how to best initialize a sequential estimation scheme given an uncertain admissible region. First, an approximate analytic probability of set membership function is defined which takes into account systemic uncertainties when assigning set membership for the admissible region. The resulting uncertain admissible region fuzzy set may then be used as a bootstrap method to initialize sequential estimation schemes. Then, the uncertain admissible region is proven to be an uninformative prior and the necessary conditions for the uncertain admissible region to be treated as a PDF are defined based on observability in the system. However, the treatment of the uncertain admissible region as an uninformative prior still requires an assumption on the a priori distribution. An evidential reasoning based sequential estimator is then developed which removes entirely the need to make assumptions on the a priori distribution of the uncertain admissible region by utilizing plausibility and belief functions. Finally a methodology is presented which enables a probabilistic association of a set of disparate sequences of unobservable measurements. This association methods uses an optimization based approach which enables a direct approximation of the PDF accompanying the state estimate in a computationally efficient way given the system is observable. The developed methodologies are tested and validated with both simulated observation data as well as experimental observation data collected with the Raven class Georgia Tech Space Object Research Telescope.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/58298
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Space situational awareness
dc.subject Uninformative priors
dc.subject Admissible regions
dc.subject State estimation
dc.subject Unobservable systems
dc.title Initialization of sequential estimation for unobservable dynamical systems using partial information in the presence of systemic uncertainty
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.corporatename College of Engineering
local.contributor.corporatename Daniel Guggenheim School of Aerospace Engineering
local.relation.ispartofseries Doctor of Philosophy with a Major in Aerospace Engineering
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
relation.isOrgUnitOfPublication a348b767-ea7e-4789-af1f-1f1d5925fb65
relation.isSeriesOfPublication f6a932db-1cde-43b5-bcab-bf573da55ed6
thesis.degree.level Doctoral
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