State filtering and parameter estimation for state space systems with scarce measurements

被引：120
作者：
Ding, Feng [1 ,2 ]
机构：
[1] Jiangnan Univ, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Peoples R China
[2] Jiangnan Univ, Control Sci & Engn Res Ctr, Wuxi 214122, Peoples R China
来源：
SIGNAL PROCESSING | 2014年 / 104卷
基金：
中国国家自然科学基金;
关键词：
Signal processing; State filtering; Parameter estimation; Recursive identification; Missing data; LEAST-SQUARES ALGORITHM; HIERARCHICAL IDENTIFICATION; RECURSIVE-IDENTIFICATION; HAMMERSTEIN SYSTEMS; OUTPUT PREDICTION; DATA OPERATION; KALMAN FILTER; CONVERGENCE; MODELS;
D O I：
10.1016/j.sigpro.2014.03.031
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper considers the state filtering and parameter estimation problems for state space systems with scarce output availability. When the scarce states are available, a least squares based algorithm and an observer based parameter estimation algorithm are developed to estimate the system parameter matrices and states. For the case with unknown states, a combined parameter estimation and state filtering algorithm is presented for canonical state space models, using the reconstructed states for the parameter estimation. Finally, an example is provided to test the effectiveness of the proposed algorithms. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码：369 / 380
页数：12
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