Joint state and multi-innovation parameter estimation for time-delay linear systems and its convergence based on the Kalman filtering

被引：109

作者：

Ding, Feng ^{[1
,2
]}

Wang, Xuehai ^{[2
,3
]}

Mao, Li ^{[2
]}

Xu, Ling ^{[2
]}

机构：

[1] Nanchang Hangkong Univ, Sch Informat Engn, Nanchang 330063, Jiangxi, Peoples R China

[2] Jiangnan Univ, Sch Internet Things Engn, Wuxi 214122, Peoples R China

[3] Xinyang Normal Univ, Coll Math & Stat, Xinyang 464000, Peoples R China

来源：

DIGITAL SIGNAL PROCESSING | 2017年 / 62卷

基金：

中国国家自然科学基金;

关键词：

Parameter estimation; Kalman filtering; Multi-innovation identification; Time-delay system; SUBSPACE IDENTIFICATION; SPACE SYSTEMS; ALGORITHM;

D O I：

10.1016/j.dsp.2016.11.010

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 studies the joint state and parameter estimation problem for a linear state space system with time-delay. A multi-innovation gradient algorithm is developed based on the Kalman filtering principle. To improve the convergence rate, a filtering based multi-innovation gradient algorithm is proposed by using the filtering technique. The analysis indicates that the parameter estimates given by the proposed algorithms converge to their true values under the persistent excitation conditions. A simulation example is given to confirm that the proposed algorithms are effective. (C) 2016 Elsevier Inc. All rights reserved.

引用

页码：211 / 223

页数：13

共 49 条

[1] On a class of parameters estimators in linear models dominating the least squares one [J].

Barone, Piero ;

Lari, Isabella .

DIGITAL SIGNAL PROCESSING, 2016, 54 :27-34

[2] Performance analysis of the generalised projection identification for time-varying systems [J].

Ding, Feng ;

Xu, Ling ;

Zhu, Quanmin .

IET CONTROL THEORY AND APPLICATIONS, 2016, 10 (18) :2506-2514

[3] Kalman state filtering based least squares iterative parameter estimation for observer canonical state space systems using decomposition [J].

Ding, Feng ;

Liu, Ximei ;

Ma, Xingyun .

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, 2016, 301 :135-143

[4] The recursive least squares identification algorithm for a class of Wiener nonlinear systems [J].

Ding, Feng ;

Liu, Ximei ;

Liu, Manman .

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2016, 353 (07) :1518-1526

[5] Recursive Least Squares Parameter Estimation for a Class of Output Nonlinear Systems Based on the Model Decomposition [J].

Ding, Feng ;

Wang, Xuehai ;

Chen, Qijia ;

Xiao, Yongsong .

CIRCUITS SYSTEMS AND SIGNAL PROCESSING, 2016, 35 (09) :3323-3338

[6] An auxiliary model based least squares algorithm for a dual-rate state space system with time-delay using the data filtering [J].

Ding, Feng ;

Liu, Ximei ;

Gu, Ya .

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2016, 353 (02) :398-408

[7] State filtering and parameter estimation for state space systems with scarce measurements [J].

Ding, Feng .

SIGNAL PROCESSING, 2014, 104 :369-380

[8] Subspace identification of bilinear systems subject to white inputs [J].

Favoreel, W ;

De Moor, B ;

Van Overschee, P .

IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1999, 44 (06) :1157-1165

[9] Array Factor Forming for Image Reconstruction of One-Dimensional Nonuniform Aperture Synthesis Radiometers [J].

Feng, Li ;

Wu, Minghu ;

Li, Qingxia ;

Chen, Ke ;

Li, Yufang ;

He, Zhangqing ;

Tong, Jing ;

Tu, Lingying ;

Xie, Honggang ;

Lu, Hailiang .

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2016, 13 (02) :237-241

[10] Parameters estimation for continuous-time heavy-tailed signals modeled by α-stable autoregressive processes [J].

Hashemifard, Zeinab ;

Amindavar, Hamidreza ;

Amini, Arash .

DIGITAL SIGNAL PROCESSING, 2016, 57 :79-92

← 1 2 3 4 5 →