Recursive Model Selection for GNSS-Combined Precise Point Positioning Algorithms / Andrew Tollefson

Tollefson, Andrew
Bib ID
vtls002096436
出版項
Ann Arbor, Michigan : ProQuest Information and learning, 2016.
稽核項
1 online resource (24 pages).
電子版
附註項
數位化論文典藏聯盟
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$a Tollefson, Andrew, $e author
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$a Recursive Model Selection for GNSS-Combined Precise Point Positioning Algorithms / $c Andrew Tollefson
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$a Ann Arbor, Michigan : $b ProQuest Information and learning, $c 2016.
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$c ©2016
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$a 1 online resource (24 pages).
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$a text $b txt $2 rdacontent
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$a Masters Abstracts International ; $v 55-03(E)
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$a Source: Masters Abstracts International, Volume: 55-03.
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$a Adviser: John Hamilton.
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$a Thesis $b (M.S.)-- $c Rochester Institute of Technology, $d 2016
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$a Includes bibliographical references
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$a Access restricted to Tamkang University users.
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$a The accuracy of Global Positioning algorithms can be improved by incorporating observations from the satellites of multiple Global Navigation Satellite Systems (GNSS). To best utilize these observations, inter-system biases must be modeled. A unified observational model is proposed which accounts for these factors for an arbitrary number of GNSS. The Bayesian Information Criterion (BIC) may be imposed upon the unified model to balance data-fitting degree with model complexity among candidate models for a given satellite configuration scenario. A simple formulation is derived for the change to the Weighted Sum Squared Residuals (WSSR) outcome caused by modifying the least-squares design matrix to accomodate additional ISB parameters. The process of updating WSSR is shown to be O(n 2), allowing a low-cost determination of the information entropy between any two candidate models. With this computationally cheap parameter selection process and a set of GNSS-heterogeneous observations, the form of the unified model with the highest expected accuracy may be efficiently selected, at a stage before matrix inversion is performed.
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$a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2016
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$a Mode of access: World Wide Web
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$a English
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$a 數位化論文典藏聯盟 $b PQDT $c 淡江大學(2017)
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$a Applied Mathematics.
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$a Geographic information science and geodesy.
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$a Electronic books. $2 local
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$a Hamilton, John, $e thesis advisor
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$a ProQuest Information and Learning Co.
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$a Rochester Institute of Technology. $b Applied Mathematics.
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$a Masters Abstracts International ; $v 55-03(E).
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$u http://info.lib.tku.edu.tw/ebook/redirect.asp?bibid=2096436 $z click for full text (PQDT)
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叢書名
Masters Abstracts International ; 55-03(E)
Masters Abstracts International ; 55-03(E).
標題
摘要
The accuracy of Global Positioning algorithms can be improved by incorporating observations from the satellites of multiple Global Navigation Satellite Systems (GNSS). To best utilize these observations, inter-system biases must be modeled. A unified observational model is proposed which accounts for these factors for an arbitrary number of GNSS. The Bayesian Information Criterion (BIC) may be imposed upon the unified model to balance data-fitting degree with model complexity among candidate models for a given satellite configuration scenario. A simple formulation is derived for the change to the Weighted Sum Squared Residuals (WSSR) outcome caused by modifying the least-squares design matrix to accomodate additional ISB parameters. The process of updating WSSR is shown to be O(n 2), allowing a low-cost determination of the information entropy between any two candidate models. With this computationally cheap parameter selection process and a set of GNSS-heterogeneous observations, the form of the unified model with the highest expected accuracy may be efficiently selected, at a stage before matrix inversion is performed.
附註
Source: Masters Abstracts International, Volume: 55-03.
Adviser: John Hamilton.
Thesis
Includes bibliographical references
English
數位化論文典藏聯盟
合著者
ISBN/ISSN
9781339423982