Document Type
Article
Publication Date
2019
College/Unit
Statler College of Engineering and Mining Resources
Department/Program/Center
Mechanical and Aerospace Engineering
Abstract
This paper presents the Quaternion-based Robust Adaptive Unscented Kalman Filter (QRAUKF) for attitude estimation. The proposed methodology modifies and extends the standard UKF equations to consistently accommodate the non-Euclidean algebra of unit quaternions and to add robustness to fast and slow variations in the measurement uncertainty. To deal with slow time-varying perturbations in the sensors, an adaptive strategy based on covariance matching that tunes the measurement covariance matrix online is used. Additionally, an outlier detector algorithm is adopted to identify abrupt changes in the UKF innovation, thus rejecting fast perturbations. Adaptation and outlier detection make the proposed algorithm robust to fast and slow perturbations such as external magnetic field interference and linear accelerations. Comparative experimental results that use an industrial manipulator robot as ground truth suggest that our method overcomes a trusted commercial solution and other widely used open source algorithms found in the literature.
Digital Commons Citation
Chiella, Antonio C. B.; Teixeira, Bruno O. S.; and Pereira, Guilherme A. S., "Quaternion-Based Robust Attitude Estimation Using an Adaptive Unscented Kalman Filter" (2019). Faculty & Staff Scholarship. 2113.
https://researchrepository.wvu.edu/faculty_publications/2113
Source Citation
Chiella, A. C. B., Teixeira, B. O. S., & Pereira, G. A. S. (2019). Quaternion-Based Robust Attitude Estimation Using an Adaptive Unscented Kalman Filter. Sensors, 19(10), 2372. https://doi.org/10.3390/s19102372
Comments
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).