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Parametrization and Prediction of EGNOS GIVD values

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Parametrization and Prediction of EGNOS GIVD values. / Mäkelä, Maija; Ali-Löytty, Simo; Müller, Philipp; Piche, Robert.

Localization and GNSS (ICL-GNSS), 2017 International Conference on. IEEE, 2018.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Mäkelä, M, Ali-Löytty, S, Müller, P & Piche, R 2018, Parametrization and Prediction of EGNOS GIVD values. in Localization and GNSS (ICL-GNSS), 2017 International Conference on. IEEE, International Conference on Localization and GNSS, 1/01/00. https://doi.org/10.1109/ICL-GNSS.2017.8376258

APA

Mäkelä, M., Ali-Löytty, S., Müller, P., & Piche, R. (2018). Parametrization and Prediction of EGNOS GIVD values. In Localization and GNSS (ICL-GNSS), 2017 International Conference on IEEE. https://doi.org/10.1109/ICL-GNSS.2017.8376258

Vancouver

Mäkelä M, Ali-Löytty S, Müller P, Piche R. Parametrization and Prediction of EGNOS GIVD values. In Localization and GNSS (ICL-GNSS), 2017 International Conference on. IEEE. 2018 https://doi.org/10.1109/ICL-GNSS.2017.8376258

Author

Mäkelä, Maija ; Ali-Löytty, Simo ; Müller, Philipp ; Piche, Robert. / Parametrization and Prediction of EGNOS GIVD values. Localization and GNSS (ICL-GNSS), 2017 International Conference on. IEEE, 2018.

Bibtex - Download

@inproceedings{25d302d3b7f94415a89b943a68346db6,
title = "Parametrization and Prediction of EGNOS GIVD values",
abstract = "Global Navigation Satellite Systems (GNSS) enable positioning almost everywhere in the world under open-sky conditions. However, the GNSS-based position is not exact due to various error sources. Errors in range measurements caused by the ionospheric delay are the largest error source in positioning with consumer grade receivers. In this paper we propose an approach for predicting the ionospheric delay for such receivers. Our method models the ionosphere by a predefined set of trigonometric basis functions, whose coefficient are predicted using a Kalman filter (KF). For the KF we introduce a new, Klobuchar-based state model, which outperforms a standard, random walk state model in our tests with real-world data for various filter update intervals. Our tests show, furthermore, that European Geostationary Navigation Overlay Service (EGNOS) transmissions can be parameterized and predicted without significant information loss, which reduces the amount of data that has to be transmitted to the receiver.",
author = "Maija M{\"a}kel{\"a} and Simo Ali-L{\"o}ytty and Philipp M{\"u}ller and Robert Piche",
year = "2018",
doi = "10.1109/ICL-GNSS.2017.8376258",
language = "English",
booktitle = "Localization and GNSS (ICL-GNSS), 2017 International Conference on",
publisher = "IEEE",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Parametrization and Prediction of EGNOS GIVD values

AU - Mäkelä, Maija

AU - Ali-Löytty, Simo

AU - Müller, Philipp

AU - Piche, Robert

PY - 2018

Y1 - 2018

N2 - Global Navigation Satellite Systems (GNSS) enable positioning almost everywhere in the world under open-sky conditions. However, the GNSS-based position is not exact due to various error sources. Errors in range measurements caused by the ionospheric delay are the largest error source in positioning with consumer grade receivers. In this paper we propose an approach for predicting the ionospheric delay for such receivers. Our method models the ionosphere by a predefined set of trigonometric basis functions, whose coefficient are predicted using a Kalman filter (KF). For the KF we introduce a new, Klobuchar-based state model, which outperforms a standard, random walk state model in our tests with real-world data for various filter update intervals. Our tests show, furthermore, that European Geostationary Navigation Overlay Service (EGNOS) transmissions can be parameterized and predicted without significant information loss, which reduces the amount of data that has to be transmitted to the receiver.

AB - Global Navigation Satellite Systems (GNSS) enable positioning almost everywhere in the world under open-sky conditions. However, the GNSS-based position is not exact due to various error sources. Errors in range measurements caused by the ionospheric delay are the largest error source in positioning with consumer grade receivers. In this paper we propose an approach for predicting the ionospheric delay for such receivers. Our method models the ionosphere by a predefined set of trigonometric basis functions, whose coefficient are predicted using a Kalman filter (KF). For the KF we introduce a new, Klobuchar-based state model, which outperforms a standard, random walk state model in our tests with real-world data for various filter update intervals. Our tests show, furthermore, that European Geostationary Navigation Overlay Service (EGNOS) transmissions can be parameterized and predicted without significant information loss, which reduces the amount of data that has to be transmitted to the receiver.

U2 - 10.1109/ICL-GNSS.2017.8376258

DO - 10.1109/ICL-GNSS.2017.8376258

M3 - Conference contribution

BT - Localization and GNSS (ICL-GNSS), 2017 International Conference on

PB - IEEE

ER -