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

页码：3562 / 3567

页数：6

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