The study of joint input and state estimation with Kalman filtering

被引：22
|
作者：
Pan, Shuwen [2 ]
Su, Hongye [2 ]
Wang, Hong [1 ,3 ]
Chu, Jian [2 ]
机构：
[1] Northeastern Univ, Shenyang, Peoples R China
[2] Zhejiang Univ, Inst Cyber Syst & Control, Hangzhou 310027, Peoples R China
[3] Univ Manchester, Sch Elect & Elect Engn, Control Syst Ctr, Manchester M60 1QD, Lancs, England
来源：
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL | 2011年 / 33卷 / 08期
基金：
中国博士后科学基金;
关键词：
input estimation; Kalman filtering; least-squares estimation; minimum variance; unbiased filtering; DISCRETE-TIME-SYSTEMS; MINIMUM-VARIANCE ESTIMATION; LEAST-SQUARES ESTIMATION; UNKNOWN INPUTS; IDENTIFICATION; EXTENSION; BIAS;
D O I：
10.1177/0142331210361551
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 investigated for discrete-time stochastic systems with direct feedthrough from unknown inputs to outputs in this paper. A Kalman filter with unknown inputs (KF-UI) approach is derived with the weighted least-squares estimation method. The least-squares estimators for states and unknown inputs are proven inherently optimal in the minimum-variance and unbiased sense. In addition, the necessary and sufficient conditions for the least-squares estimation of unknown inputs and states are provided and it has been shown that no prior information of unknown inputs is required for the proposed KF-UI approach. Simulation results for a linear system are included to demonstrate the effectiveness and optimality of the proposed KF-UI approach.
引用
收藏
页码：901 / 918
页数：18
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