The Study on An General Kalman Filter with Unknown Inputs

被引：0
|
作者：
Pan, Shuwen [1 ]
Du, Pengying [1 ]
Li, Yanjun [1 ]
Chen, Zuo [1 ]
Wang, Hong
机构：
[1] Zhejiang Univ City Coll, Key Lab Intelligent Syst, Hangzhou 310015, Zhejiang, Peoples R China
来源：
2014 11TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA) | 2014年
关键词：
Kalman filtering; estimation; Unknown Inputs; MINIMUM-VARIANCE ESTIMATION; STATE ESTIMATION; SYSTEMS;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The problem of joint input and state estimation is discussed in this paper for linear discrete-time stochastic systems. By minimizing an objective function of weighted least squares estimation with respect to the states and unknown inputs, a recursive filter approach referred to as General Kalman filter with unknown inputs (GKF-UI) is obtained. It is shown that the proposed GKF-UI approach covers more general observation cases over the previous Kalman filter approaches in the literature to provide uniquely optimum in sense of both least-squares (LS) and minimum-variance biased (MUV). Due to the limit of space, the numerical example is omitted.
引用
收藏
页码：3562 / 3567
页数：6
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