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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.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Recursive Model Selection for GNSS-Combined Precise Point Positioning Algorithms./
作者:	Tollefson, Andrew.
面頁冊數:	24 p.
附註:	Source: Masters Abstracts International, Volume: 55-03.
Contained By:	Masters Abstracts International55-03(E).
標題:	Applied mathematics. -
電子資源:	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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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.
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