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A Class of Quaternion Kalman Filters.
- Source :
-
IEEE Transactions on Neural Networks & Learning Systems . Mar2014, Vol. 25 Issue 3, p533-544. 12p. - Publication Year :
- 2014
-
Abstract
- The existing Kalman filters for quaternion-valued signals do not operate fully in the quaternion domain, and are combined with the real Kalman filter to enable the tracking in 3-D spaces. Using the recently introduced HR-calculus, we develop the fully quaternion-valued Kalman filter (QKF) and quaternion-extended Kalman filter (QEKF), allowing for the tracking of 3-D and 4-D signals directly in the quaternion domain. To consider the second-order noncircularity of signals, we employ the recently developed augmented quaternion statistics to derive the widely linear QKF (WL-QKF) and widely linear QEKF (WL-QEKF). To reduce computational requirements of the widely linear algorithms, their efficient implementation are proposed and it is shown that the quaternion widely linear model can be simplified when processing 3-D data, further reducing the computational requirements. Simulations using both synthetic and real-world circular and noncircular signals illustrate the advantages offered by widely linear over strictly linear quaternion Kalman filters. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 2162237X
- Volume :
- 25
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Neural Networks & Learning Systems
- Publication Type :
- Periodical
- Accession number :
- 94587245
- Full Text :
- https://doi.org/10.1109/TNNLS.2013.2277540