Quaternion-based filtering for gyroless attitude estimation without an attitude dynamics model

• Autorzy: Zhang S. , Xing F. , Sun T. , You Z.

Identyfikatory

DOI

10.24425/123903

• Języki publikacji: EN

Abstrakty

EN

Conventionally, the filtering technique for attitude estimation is performed using gyros or attitude dynamics models. In order to extend the application range of an attitude filter, this paper proposes a quaternion-based filtering framework for gyroless attitude estimation without an attitude dynamics model. The attitude estimation system is established based on a quaternion kinematic equation and vector observation models. The angular velocity in the system is determined through observation vectors from attitude sensors and the statistical properties of the angular velocity error are analysed. A Kalman filter is applied to estimate the attitude error such that the effect from the angular velocity error is compensated with its statistical properties at each sampling moment. A numerical simulation example is presented to illustrate the performance of the proposed algorithm.

Słowa kluczowe

EN

quaternion-based filtering; gyroless attitude estimation; angular velocity determination; Kalman filter

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2018
• Tom: Vol. 25, nr 3
• Strony: 631--643
• Opis fizyczny: Bibliogr. 25 poz., rys., wykr., wzory

Twórcy

autor

Zhang S.

• Tsinghua University, Department of Precision Instrument, Beijing 100084, China

autor

Xing F.

• Tsinghua University, State Key Laboratory of Precision Measurement Technology and Instrument, Beijing 100084, China

autor

Sun T.

• Tsinghua University, Department of Precision Instrument, Beijing 100084, China

autor

You Z.

• Tsinghua University, Department of Precision Instrument, Beijing 100084, China

Bibliografia

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Uwagi

EN

1. This work was supported by the Natural Science Foundation of China (grant # 61377012 and 51522505), by the Key Research and Development Program of China (grant # 2016YFB0501201), and by the Postdoctoral Science Foundation of China (grant # 2017M610882).

2. Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).