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Does RAIM with Correct Exclusion Produce Unbiased Positions?

As the navigation solution of exclusion-based RAIM follows from a combination of least-squares estimation and a statistically based exclusion-process, the computation of the integrity of the navigation solution has to take the propagated uncertainty of the combined estimation-testing procedure into...

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Detalles Bibliográficos
Autores principales: Teunissen, Peter J. G., Imparato, Davide, Tiberius, Christian C. J. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5539570/
https://www.ncbi.nlm.nih.gov/pubmed/28672862
http://dx.doi.org/10.3390/s17071508
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author Teunissen, Peter J. G.
Imparato, Davide
Tiberius, Christian C. J. M.
author_facet Teunissen, Peter J. G.
Imparato, Davide
Tiberius, Christian C. J. M.
author_sort Teunissen, Peter J. G.
collection PubMed
description As the navigation solution of exclusion-based RAIM follows from a combination of least-squares estimation and a statistically based exclusion-process, the computation of the integrity of the navigation solution has to take the propagated uncertainty of the combined estimation-testing procedure into account. In this contribution, we analyse, theoretically as well as empirically, the effect that this combination has on the first statistical moment, i.e., the mean, of the computed navigation solution. It will be shown, although statistical testing is intended to remove biases from the data, that biases will always remain under the alternative hypothesis, even when the correct alternative hypothesis is properly identified. The a posteriori exclusion of a biased satellite range from the position solution will therefore never remove the bias in the position solution completely.
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spelling pubmed-55395702017-08-11 Does RAIM with Correct Exclusion Produce Unbiased Positions? Teunissen, Peter J. G. Imparato, Davide Tiberius, Christian C. J. M. Sensors (Basel) Article As the navigation solution of exclusion-based RAIM follows from a combination of least-squares estimation and a statistically based exclusion-process, the computation of the integrity of the navigation solution has to take the propagated uncertainty of the combined estimation-testing procedure into account. In this contribution, we analyse, theoretically as well as empirically, the effect that this combination has on the first statistical moment, i.e., the mean, of the computed navigation solution. It will be shown, although statistical testing is intended to remove biases from the data, that biases will always remain under the alternative hypothesis, even when the correct alternative hypothesis is properly identified. The a posteriori exclusion of a biased satellite range from the position solution will therefore never remove the bias in the position solution completely. MDPI 2017-06-26 /pmc/articles/PMC5539570/ /pubmed/28672862 http://dx.doi.org/10.3390/s17071508 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Teunissen, Peter J. G.
Imparato, Davide
Tiberius, Christian C. J. M.
Does RAIM with Correct Exclusion Produce Unbiased Positions?
title Does RAIM with Correct Exclusion Produce Unbiased Positions?
title_full Does RAIM with Correct Exclusion Produce Unbiased Positions?
title_fullStr Does RAIM with Correct Exclusion Produce Unbiased Positions?
title_full_unstemmed Does RAIM with Correct Exclusion Produce Unbiased Positions?
title_short Does RAIM with Correct Exclusion Produce Unbiased Positions?
title_sort does raim with correct exclusion produce unbiased positions?
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5539570/
https://www.ncbi.nlm.nih.gov/pubmed/28672862
http://dx.doi.org/10.3390/s17071508
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