DocumentCode :
36366
Title :
A Class of Quaternion Kalman Filters
Author :
Jahanchahi, Cyrus ; Mandic, Danilo P.
Author_Institution :
Commun. & Signal Process. Res. Group, Imperial Coll. London, London, UK
Volume :
25
Issue :
3
fYear :
2014
fDate :
Mar-14
Firstpage :
533
Lastpage :
544
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.
Keywords :
Kalman filters; statistics; 3D signal tracking; 3D spaces; 4D signal tracking; HR-calculus; WL-QEKF; linear quaternion Kalman filters; quaternion statistics; quaternion-extended Kalman filter; quaternion-valued Kalman filter; quaternion-valued signals; signal second-order noncircularity; widely linear QEKF; Computational modeling; Covariance matrices; Kalman filters; Learning systems; Quaternions; Solid modeling; Vectors; Extended Kalman filter; improperness; quaternion Kalman filters; quaternion augmented statistics; quaternion noncircularity; state space prediction; trajectory tracking; widely linear model; wind modeling;
fLanguage :
English
Journal_Title :
Neural Networks and Learning Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
2162-237X
Type :
jour
DOI :
10.1109/TNNLS.2013.2277540
Filename :
6617675
Link To Document :
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