TY - GEN
T1 - Bayesian unscented Kalman filter for state estimation of nonlinear and non-Gaussian systems
AU - Liu, Zhong
AU - Chan, Shing Chow
AU - Wu, Ho Chun
AU - Wu, Jiafei
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/28
Y1 - 2016/11/28
N2 - This paper proposes a Bayesian unscented Kalman filter with simplified Gaussian mixtures (BUKF-SGM) for dynamic state space estimation of nonlinear and non-Gaussian systems. In the BUKF-SGM, the state and noise densities are approximated as finite Gaussian mixtures, in which the mean and covariance for each component are recursively estimated using the UKF. To avoid the exponential growth of mixture components, a Gaussian mixture simplification algorithm is employed to reduce the number of mixture components, which leads to lower complexity in comparing with conventional resampling and clustering techniques. Experimental results show that the proposed BUKF-SGM can achieve better performance compared with the particle filter (PF)-based algorithms. This provides an attractive alternative for nonlinear state estimation problem.
AB - This paper proposes a Bayesian unscented Kalman filter with simplified Gaussian mixtures (BUKF-SGM) for dynamic state space estimation of nonlinear and non-Gaussian systems. In the BUKF-SGM, the state and noise densities are approximated as finite Gaussian mixtures, in which the mean and covariance for each component are recursively estimated using the UKF. To avoid the exponential growth of mixture components, a Gaussian mixture simplification algorithm is employed to reduce the number of mixture components, which leads to lower complexity in comparing with conventional resampling and clustering techniques. Experimental results show that the proposed BUKF-SGM can achieve better performance compared with the particle filter (PF)-based algorithms. This provides an attractive alternative for nonlinear state estimation problem.
KW - Bayesian unscented Kalman filter
KW - Dynamic state estimation
KW - Gaussian mixture
KW - Nonlinear and non-Gaussian system
KW - Particle filter
UR - http://www.scopus.com/inward/record.url?scp=85005979593&partnerID=8YFLogxK
U2 - 10.1109/EUSIPCO.2016.7760287
DO - 10.1109/EUSIPCO.2016.7760287
M3 - Conference contribution
AN - SCOPUS:85005979593
T3 - European Signal Processing Conference
SP - 443
EP - 447
BT - 2016 24th European Signal Processing Conference, EUSIPCO 2016
T2 - 24th European Signal Processing Conference, EUSIPCO 2016
Y2 - 28 August 2016 through 2 September 2016
ER -