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Metrology and Measurement Systems

The Journal of Committee on Metrology and Scientific Instrumentation of Polish Academy of Sciences

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IMPACT FACTOR 2016: 1.598

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2300-1941
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Volume 24, Issue 1

Issues

Nanosatellite Attitude Estimation from Vector Measurements Using SVD-Aided UKF Algorithm

Demet Cilden
  • Corresponding author
  • 1) Istanbul Technical University, Faculty of Aeronautics and Astronautics, Maslak, 34469, Istanbul, Turkey
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  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Halil Ersin Soken
  • 2) Japan Aerospace Exploration Agency (JAXA), Institute of Space and Astronautical Science (ISAS), Sagamihara, Japan
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  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Chingiz Hajiyev
Published Online: 2017-03-20 | DOI: https://doi.org/10.1515/mms-2017-0011

Abstract

The integrated Singular Value Decomposition (SVD) and Unscented Kalman Filter (UKF) method can recursively estimate the attitude and attitude rates of a nanosatellite. At first, Wahba’s loss function is minimized using the SVD and the optimal attitude angles are determined on the basis of the magnetometer and Sun sensor measurements. Then, the UKF makes use of the SVD’s attitude estimates as measurement results and provides more accurate attitude information as well as the attitude rate estimates. The elements of “Rotation angle error covariance matrix” calculated for the SVD estimations are used in the UKF as the measurement noise covariance values. The algorithm is compared with the SVD and UKF only methods for estimating the attitude from vector measurements. Possible algorithm switching ideas are discussed especially for the eclipse period, when the Sun sensor measurements are not available.

Keywords: attitude estimation; nanosatellite; UKF; SVD; SVD-Aided UKF

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About the article

Received: 2016-03-15

Accepted: 2016-09-24

Published Online: 2017-03-20

Published in Print: 2017-03-01


Citation Information: Metrology and Measurement Systems, Volume 24, Issue 1, Pages 113–125, ISSN (Online) 2300-1941, DOI: https://doi.org/10.1515/mms-2017-0011.

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© 2017 Polish Academy of Sciences. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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