Adaptive variational Bayesian cubature Kalman filtering

被引：0

作者：

Shen, Feng ^{[1
]}

Xu, Guang-Hui ^{[1
]}

Sang, Jing ^{[1
]}

机构：

[1] College of Automation, Harbin Engineering University, Harbin

来源：

Dianji yu Kongzhi Xuebao/Electric Machines and Control | 2015年 / 19卷 / 04期

关键词：

Adaptive; Cubature Kalman filtering; Non-linear system; Variational Bayes;

D O I：

10.15938/j.emc.2015.04.015

中图分类号：

N94 [系统科学]; C94 [];

学科分类号：

0711 ; 081103 ; 1201 ;

摘要：

Focusing on the performance of Cubature Kalman filtering may be degraded due to the fact that in practical situations the statistics of measurement noise might change. An adaptive variational Bayesian cubature Kalman filtering algorithm was proposed which can be used in non-linear system models. In each update step of proposed method, both system state and time-variant measurement noise were recognized as random variables to estimate. Measurements noise variances were approximated by variational Bayes, thereafter, system states were updated by cubature Kalman filtering. Simulation results demonstrate the proposed filter can well track measurement noise for a non-linear system and outperforms cubature Kalman filter. ©, 2015, Editorial Department of Electric Machines and Control. All right reserved.

引用

页码：94 / 99

页数：5

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