TY - JOUR
T1 - Robust Cubature Kalman Filter With Gaussian-Multivariate Laplacian Mixture Distribution and Partial Variational Bayesian Method
AU - Fu, Hongpo
AU - Huang, Wei
AU - Li, Zhenwei
AU - Cheng, Yongmei
AU - Zhang, Tianyi
N1 - Publisher Copyright:
© 1991-2012 IEEE.
PY - 2023
Y1 - 2023
N2 - This article explores the problem of nonlinear state estimation in the presence of outlier-contaminated measurements. First, to deal with the non-stationary non-Gaussian noises caused by randomly occurring measurement outliers, we propose a new Gaussian-multivariate Laplacian mixture (GMLM) distribution and construct it as a hierarchical Gaussian expression. Next, utilizing the GMLM distribution and existing variational Bayesian (VB) method, a robust cubature Kalman filter is derived (VB-GMLMRCKF). Then, considering the high computational complexity of the existing VB inference process, a new partial VB (PVB) method is developed, which can separately estimate state vector and mismatched measurement noise covariance matrix. Building upon the VB-GMLMRCKF and PVB approach, a novel robust cubature Kalman filter is derived (PVB-GMLMRCKF). Finally, a target tracking model is utilized to evaluate the PVB-GMLMRCKF in terms of estimation accuracy, estimation consistency and computational efficiency.
AB - This article explores the problem of nonlinear state estimation in the presence of outlier-contaminated measurements. First, to deal with the non-stationary non-Gaussian noises caused by randomly occurring measurement outliers, we propose a new Gaussian-multivariate Laplacian mixture (GMLM) distribution and construct it as a hierarchical Gaussian expression. Next, utilizing the GMLM distribution and existing variational Bayesian (VB) method, a robust cubature Kalman filter is derived (VB-GMLMRCKF). Then, considering the high computational complexity of the existing VB inference process, a new partial VB (PVB) method is developed, which can separately estimate state vector and mismatched measurement noise covariance matrix. Building upon the VB-GMLMRCKF and PVB approach, a novel robust cubature Kalman filter is derived (PVB-GMLMRCKF). Finally, a target tracking model is utilized to evaluate the PVB-GMLMRCKF in terms of estimation accuracy, estimation consistency and computational efficiency.
KW - Gaussian-multivariate laplacian mixture distribution
KW - non-Gaussian noises
KW - Nonlinear state estimation
KW - variational Bayesian
UR - http://www.scopus.com/inward/record.url?scp=85151508911&partnerID=8YFLogxK
U2 - 10.1109/TSP.2023.3256041
DO - 10.1109/TSP.2023.3256041
M3 - 文章
AN - SCOPUS:85151508911
SN - 1053-587X
VL - 71
SP - 847
EP - 858
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
ER -