The modified extended Kalman filter based recursive estimation for Wiener nonlinear systems with process noise and measurement noise

被引：13
|
作者：
Wang, Xuehai [1 ]
Zhu, Fang [2 ]
Ding, Feng [3 ]
机构：
[1] Xinyang Normal Univ, Sch Math & Stat, Xinyang 464000, Peoples R China
[2] Xinyang Normal Univ, Sch Educ Sci, Xinyang, Peoples R China
[3] Qingdao Univ Sci & Technol, Coll Automat & Elect Engn, Qingdao, Peoples R China
来源：
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING | 2020年 / 34卷 / 10期
基金：
中国国家自然科学基金;
关键词：
Kalman filter; least squares; multi-innovation identification; parameter estimation; Wiener system; PARAMETER-ESTIMATION ALGORITHM; STATE-SPACE SYSTEMS; FAULT-DETECTION; IDENTIFICATION; COVARIANCE; STRATEGY;
D O I：
10.1002/acs.3148
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This article develops the modified extended Kalman filter based recursive estimation algorithms for Wiener nonlinear systems with process noise and measurement noise. The prior estimate of the linear block output is computed based on the auxiliary model, and the posterior estimate is updated by designing a modified extended Kalman filter. A multi-innovation gradient algorithm and a recursive least squares algorithm are derived to estimate the parameters of the linear subsystem, respectively. The simulation examples are provided to demonstrate the effectiveness of the proposed algorithms.
引用
收藏
页码：1321 / 1340
页数：20
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