Estimation of attitude using robust adaptive kalman filter

Date

2021-06-23

Author

Candan, Batu
Söken, Halil Ersin

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IEEE.This paper proposes a novel covariance-scaling based robust adaptive Kalman filter (RAKF) algorithm for attitude (i.e., roll and pitch) estimation using an inertial measurement unit (IMU) composed of accelerometer and gyroscope triads. KF based and complementary filtering (CF) based approaches are the two common methods for solving the attitude estimation problem. Efficiency and optimality of the KF based attitude filters are correlated with appropriate tuning of the covariance matrices. Manual tuning process is difficult and time-consuming task. The proposed algorithm provides an adaptive way for tuning process and it can accurately estimate the attitude in two axes. The proposed methodology is tested and compared with other existing filtering methodologies in the literature under different dynamical conditions and using real-world experimental dataset in order to validate its effectiveness.

Subject Keywords

Accelerometer, Attitude estimation, Gyroscope, Inertial sensors, Robust adaptive Kalman filter (RAKF)

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85113347782&origin=inward
https://hdl.handle.net/11511/93741

DOI

https://doi.org/10.1109/metroaerospace51421.2021.9511658

Conference Name

8th IEEE International Workshop on Metrology for AeroSpace, MetroAeroSpace 2021

Collections

Department of Aerospace Engineering, Conference / Seminar

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Citation Formats

B. Candan and H. E. Söken, “Estimation of attitude using robust adaptive kalman filter,” presented at the 8th IEEE International Workshop on Metrology for AeroSpace, MetroAeroSpace 2021, Virtual, Online, 2021, Accessed: 00, 2021. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85113347782&origin=inward.