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Recursive Model Selection for GNSS-C...

Tollefson, Andrew.

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Recursive Model Selection for GNSS-Combined Precise Point Positioning Algorithms.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Recursive Model Selection for GNSS-Combined Precise Point Positioning Algorithms./
Author:	Tollefson, Andrew.
Description:	24 p.
Notes:	Source: Masters Abstracts International, Volume: 55-03.
Contained By:	Masters Abstracts International55-03(E).
Subject:	Applied mathematics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10003077
ISBN:	9781339423982

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

Recursive Model Selection for GNSS-Combined Precise Point Positioning Algorithms. - 24 p.

Source: Masters Abstracts International, Volume: 55-03.

Thesis (M.S.)--Rochester Institute of Technology, 2016.

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.

ISBN: 9781339423982Subjects--Topical Terms:

2122814
Applied mathematics.

Recursive Model Selection for GNSS-Combined Precise Point Positioning Algorithms.
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100 1 $a Tollefson, Andrew. $3 3276393
245 1 0 $a Recursive Model Selection for GNSS-Combined Precise Point Positioning Algorithms.
300 $a 24 p.
500 $a Source: Masters Abstracts International, Volume: 55-03.
500 $a Adviser: John Hamilton.
502 $a Thesis (M.S.)--Rochester Institute of Technology, 2016.
520 $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.
590 $a School code: 0465.
650 4 $a Applied mathematics. $3 2122814
650 4 $a Geographic information science and geodesy. $3 2122917
690 $a 0364
690 $a 0370
710 2 $a Rochester Institute of Technology. $b Applied Mathematics. $3 1679315
773 0 $t Masters Abstracts International $g 55-03(E).
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856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10003077