Variational Bayesian Two-Stage Kalman Filter for Systems with Unknown Inputs

被引：5

作者：

Sun, Junlong ^{[1
]}

Zhou, Jie ^{[1
]}

Gu, Xuekui ^{[1
]}

机构：

[1] Sichuan Univ, Coll Math, Chengdu 610064, Sichuan, Peoples R China

来源：

2012 INTERNATIONAL WORKSHOP ON INFORMATION AND ELECTRONICS ENGINEERING | 2012年 / 29卷

关键词：

variational Bayes method; unknown inputs; dynamic bias; Bayes filter; two-stage Kalman filter;

D O I：

10.1016/j.proeng.2012.01.299

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

In this paper, we consider the probabilistic approach in adaptive state estimation for discrete-time linear stochastic time-varying systems with unknown inputs. When lack knowledge of noise, we regard the system state, unknown inputs and time-varying noise parameters as hidden variables with conjugate priors, and use variational Bayes method to side-step the complicated Bayesian inference. The proposed algorithm is based on optimal two-stage Kalman filtering technique that enables us to obtain marginal posterior distributions of hidden variables. A numerical example is provided to demonstrate the state estimation performance of proposed algorithm. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Harbin University of Science and Technology

引用

页码：2265 / 2273

页数：9

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