Adaptive fading Kalman filter with an application

ISCAS OpenIR

	Adaptive fading Kalman filter with an application
	Qijun Xia; Ming Rao; Yiqun Ying; Xuemin Shen
	1994
发表期刊	Automatica
卷号	30 期号:8 页码:1333-1338
摘要	A new adaptive state estimation algorithm, namely adaptive fading Kalman filter (AFKF), is proposed to solve the divergence problem of Kalman filter. A criterion function is constructed to measure the optimality of Kalman filter. The forgetting factor in AFKF is adaptively adjusted by minimizing the defined criterion function using measured outputs. The algorithm remains convergent and tends to be optimal in the presence of model errors. It has been successfully applied to the headbox of a paper-making machine for state estimation.
收录类别	其他
合作性质	其它
关键词	Kalman Filter State Estimation Adaptive Estimation Discrete System Industrial Processes
语种	英语
内容类型	期刊论文
URI标识	http://ir.iscas.ac.cn/handle/311060/1341
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Qijun Xia,Ming Rao,Yiqun Ying,et al. Adaptive fading Kalman filter with an application[J]. Automatica,1994,30(8):1333-1338.
APA	Qijun Xia,Ming Rao,Yiqun Ying,&Xuemin Shen.(1994).Adaptive fading Kalman filter with an application.Automatica,30(8),1333-1338.
MLA	Qijun Xia,et al."Adaptive fading Kalman filter with an application".Automatica 30.8(1994):1333-1338.