EKF for wind speed estimation and sensor fault detection using pitot tube measurements

Chingiz Hajiyev; Demet Cilden-Guler; Ulviye Hacizade

doi:10.1109/RAST.2019.8767902

EKF for wind speed estimation and sensor fault detection using pitot tube measurements

Chingiz Hajiyev, Demet Cilden-Guler, Ulviye Hacizade

Halic University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

The wind speed is estimated by Kalman filter using GPS and Air Data System (ADS) measurements. For this purpose, Extended Kalman Filter (EKF) was designed, and as state variables, the wind velocity components and ADS pitot scale factor are considered. A sensor fault detection algorithm based on EKF innovation process was developed. The results were obtained for noise increment and bias conditions. Estimation errors, normalized innovations and fault detection statistics were obtained.

Original language	English
Title of host publication	Proceedings of 9th International Conference on Recent Advances in Space Technologies, RAST 2019
Editors	S. Menekay, O. Cetin, O. Alparslan
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	887-893
Number of pages	7
ISBN (Electronic)	9781538694480
DOIs	https://doi.org/10.1109/RAST.2019.8767902
Publication status	Published - Jun 2019
Event	9th International Conference on Recent Advances in Space Technologies, RAST 2019 - Istanbul, Turkey Duration: 11 Jun 2019 → 14 Jun 2019

Publication series

Name	Proceedings of 9th International Conference on Recent Advances in Space Technologies, RAST 2019

Conference

Conference	9th International Conference on Recent Advances in Space Technologies, RAST 2019
Country/Territory	Turkey
City	Istanbul
Period	11/06/19 → 14/06/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Funding

ACKNOWLEDGMENT This research is part of the project numbered with TM3061 which is fully financed by Turkish Aerospace Inc. In order to learn for more details about the project, please apply/refer to Turkish Aerospace Inc.

Funders	Funder number
Turkish Aerospace Inc.

Keywords

GPS
estimation
fault detection
pitot tube
wind speed

Access to Document

10.1109/RAST.2019.8767902

Cite this

Hajiyev, C., Cilden-Guler, D., & Hacizade, U. (2019). EKF for wind speed estimation and sensor fault detection using pitot tube measurements. In S. Menekay, O. Cetin, & O. Alparslan (Eds.), Proceedings of 9th International Conference on Recent Advances in Space Technologies, RAST 2019 (pp. 887-893). Article 8767902 (Proceedings of 9th International Conference on Recent Advances in Space Technologies, RAST 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RAST.2019.8767902

Hajiyev, Chingiz ; Cilden-Guler, Demet ; Hacizade, Ulviye. / EKF for wind speed estimation and sensor fault detection using pitot tube measurements. Proceedings of 9th International Conference on Recent Advances in Space Technologies, RAST 2019. editor / S. Menekay ; O. Cetin ; O. Alparslan. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 887-893 (Proceedings of 9th International Conference on Recent Advances in Space Technologies, RAST 2019).

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title = "EKF for wind speed estimation and sensor fault detection using pitot tube measurements",

abstract = "The wind speed is estimated by Kalman filter using GPS and Air Data System (ADS) measurements. For this purpose, Extended Kalman Filter (EKF) was designed, and as state variables, the wind velocity components and ADS pitot scale factor are considered. A sensor fault detection algorithm based on EKF innovation process was developed. The results were obtained for noise increment and bias conditions. Estimation errors, normalized innovations and fault detection statistics were obtained.",

keywords = "GPS, estimation, fault detection, pitot tube, wind speed",

author = "Chingiz Hajiyev and Demet Cilden-Guler and Ulviye Hacizade",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 9th International Conference on Recent Advances in Space Technologies, RAST 2019 ; Conference date: 11-06-2019 Through 14-06-2019",

year = "2019",

month = jun,

doi = "10.1109/RAST.2019.8767902",

language = "English",

series = "Proceedings of 9th International Conference on Recent Advances in Space Technologies, RAST 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "887--893",

editor = "S. Menekay and O. Cetin and O. Alparslan",

booktitle = "Proceedings of 9th International Conference on Recent Advances in Space Technologies, RAST 2019",

address = "United States",

}

Hajiyev, C , Cilden-Guler, D & Hacizade, U 2019, EKF for wind speed estimation and sensor fault detection using pitot tube measurements. in S Menekay, O Cetin & O Alparslan (eds), Proceedings of 9th International Conference on Recent Advances in Space Technologies, RAST 2019., 8767902, Proceedings of 9th International Conference on Recent Advances in Space Technologies, RAST 2019, Institute of Electrical and Electronics Engineers Inc., pp. 887-893, 9th International Conference on Recent Advances in Space Technologies, RAST 2019, Istanbul, Turkey, 11/06/19. https://doi.org/10.1109/RAST.2019.8767902

EKF for wind speed estimation and sensor fault detection using pitot tube measurements. / Hajiyev, Chingiz ; Cilden-Guler, Demet; Hacizade, Ulviye.
Proceedings of 9th International Conference on Recent Advances in Space Technologies, RAST 2019. ed. / S. Menekay; O. Cetin; O. Alparslan. Institute of Electrical and Electronics Engineers Inc., 2019. p. 887-893 8767902 (Proceedings of 9th International Conference on Recent Advances in Space Technologies, RAST 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - The wind speed is estimated by Kalman filter using GPS and Air Data System (ADS) measurements. For this purpose, Extended Kalman Filter (EKF) was designed, and as state variables, the wind velocity components and ADS pitot scale factor are considered. A sensor fault detection algorithm based on EKF innovation process was developed. The results were obtained for noise increment and bias conditions. Estimation errors, normalized innovations and fault detection statistics were obtained.

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Hajiyev C , Cilden-Guler D, Hacizade U. EKF for wind speed estimation and sensor fault detection using pitot tube measurements. In Menekay S, Cetin O, Alparslan O, editors, Proceedings of 9th International Conference on Recent Advances in Space Technologies, RAST 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 887-893. 8767902. (Proceedings of 9th International Conference on Recent Advances in Space Technologies, RAST 2019). doi: 10.1109/RAST.2019.8767902

EKF for wind speed estimation and sensor fault detection using pitot tube measurements

Abstract

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Bibliographical note

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Keywords

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