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

• Autorzy: Cilden D. , Soken H. E. , Hajiev C.

Identyfikatory

DOI

10.1515/mms-2017-0011

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

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

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2017
• Tom: Vol. 24, nr 1
• Strony: 113--125
• Opis fizyczny: Bibliogr. 25 poz., rys., tab., wykr., wzory

Twórcy

autor

Cilden D.

• Istanbul Technical University, Faculty of Aeronautics and Astronautics, Maslak, 34469, Istanbul, Turkey

autor

Soken H. E.

• Japan Aerospace Exploration Agency (JAXA), Institute of Space and Astronautical Science (ISAS), Sagamihara, Japan

autor

Hajiev C.

• Istanbul Technical University, Faculty of Aeronautics and Astronautics, Maslak, 34469, Istanbul, Turkey

Bibliografia

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Uwagi

EN

The work was supported by TUBITAK (The Scientific and Technological Research Council of Turkey), Grant 113E595.

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).