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.
Library of Congress Subject Headings
Global Positioning System--Data processing
Applied and Computational Mathematics (MS)
Department, Program, or Center
School of Mathematical Sciences (COS)
Tollefson, Andrew, "Recursive Model Selection for GNSS-Combined Precise Point Positioning Algorithms" (2015). Thesis. Rochester Institute of Technology. Accessed from
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