@article{MAKHILLIJSSCEA202114428834,
    title = {An Improved Real-Time Adaptive Constrained Quaternion Extended Kalman Filter},
    journal = {International Journal of System Signal Control and Engineering Application},
    volume = {14},
    number = {4},
    pages = {57-66},
    year = {2021},
    issn = {1997-5422},
    doi = {ijssceapp.2021.57.66},
    url = {https://makhillpublications.co/view-article.php?issn=1997-5422&doi=ijssceapp.2021.57.66},
    author = {Iyad},
    keywords = {Extended Kalman filer,adaptive extended Kalman filter,covariance matching,quaternion},
    abstract = {In this study, a new improved real time
Adaptive Constrained Quaternion Extended Kalman Filter
(ACQEKF) algorithm is proposed. It is employed to
estimate the quaternion and bias states of a constrained
nonlinear system perturbed by noise using noisy
measurements. The values of the process and
measurement noise covariances Q and R, respectively are
unknown or partially known, their biased initializations
result in the degradation or divergence of the quaternion
Extended Kalman Filter (EKF) performance. This study
proposes a new method to improve the EKF performance
against the covariances uncertainty. Unlike, the previous
methods, this method adopts the idea of the recursive
estimation of the EKF to propose two tunable recursive
updating rules for Q and R, respectively designed based
on the filter innovations. As for the quaternion constraint,
it is projected onto the EKF gain derivation. The proposed
ACQEKF proved itself to have a dramatic improved
performance over the conventional EKF, the estimates are
more accurate have less noise and more stable.}
    }