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Complete Systematic Error Model of SSR for Sensor Registration in ATC Surveillance Networks

In this paper, a complete and rigorous mathematical model for secondary surveillance radar systematic errors (biases) is developed. The model takes into account the physical effects systematically affecting the measurement processes. The azimuth biases are calculated from the physical error of the a...

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Autores principales: Jarama, Ángel J., López-Araquistain, Jaime, de Miguel, Gonzalo, Besada, Juan A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5677430/
https://www.ncbi.nlm.nih.gov/pubmed/28934157
http://dx.doi.org/10.3390/s17102171
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author Jarama, Ángel J.
López-Araquistain, Jaime
de Miguel, Gonzalo
Besada, Juan A.
author_facet Jarama, Ángel J.
López-Araquistain, Jaime
de Miguel, Gonzalo
Besada, Juan A.
author_sort Jarama, Ángel J.
collection PubMed
description In this paper, a complete and rigorous mathematical model for secondary surveillance radar systematic errors (biases) is developed. The model takes into account the physical effects systematically affecting the measurement processes. The azimuth biases are calculated from the physical error of the antenna calibration and the errors of the angle determination dispositive. Distance bias is calculated from the delay of the signal produced by the refractivity index of the atmosphere, and from clock errors, while the altitude bias is calculated taking into account the atmosphere conditions (pressure and temperature). It will be shown, using simulated and real data, that adapting a classical bias estimation process to use the complete parametrized model results in improved accuracy in the bias estimation.
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spelling pubmed-56774302017-11-17 Complete Systematic Error Model of SSR for Sensor Registration in ATC Surveillance Networks Jarama, Ángel J. López-Araquistain, Jaime de Miguel, Gonzalo Besada, Juan A. Sensors (Basel) Article In this paper, a complete and rigorous mathematical model for secondary surveillance radar systematic errors (biases) is developed. The model takes into account the physical effects systematically affecting the measurement processes. The azimuth biases are calculated from the physical error of the antenna calibration and the errors of the angle determination dispositive. Distance bias is calculated from the delay of the signal produced by the refractivity index of the atmosphere, and from clock errors, while the altitude bias is calculated taking into account the atmosphere conditions (pressure and temperature). It will be shown, using simulated and real data, that adapting a classical bias estimation process to use the complete parametrized model results in improved accuracy in the bias estimation. MDPI 2017-09-21 /pmc/articles/PMC5677430/ /pubmed/28934157 http://dx.doi.org/10.3390/s17102171 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
Jarama, Ángel J.
López-Araquistain, Jaime
de Miguel, Gonzalo
Besada, Juan A.
Complete Systematic Error Model of SSR for Sensor Registration in ATC Surveillance Networks
title Complete Systematic Error Model of SSR for Sensor Registration in ATC Surveillance Networks
title_full Complete Systematic Error Model of SSR for Sensor Registration in ATC Surveillance Networks
title_fullStr Complete Systematic Error Model of SSR for Sensor Registration in ATC Surveillance Networks
title_full_unstemmed Complete Systematic Error Model of SSR for Sensor Registration in ATC Surveillance Networks
title_short Complete Systematic Error Model of SSR for Sensor Registration in ATC Surveillance Networks
title_sort complete systematic error model of ssr for sensor registration in atc surveillance networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5677430/
https://www.ncbi.nlm.nih.gov/pubmed/28934157
http://dx.doi.org/10.3390/s17102171
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