State estimators for systems with random parameter matrices, stochastic nonlinearities, fading measurements and correlated noises

被引：49

作者：

Sun, Shuli ^{[1
]}

Tian, Tian ^{[1
]}

Lin Honglei ^{[1
]}

机构：

[1] Heilongjiang Univ, Sch Elect Engn, Harbin 150080, Peoples R China

来源：

INFORMATION SCIENCES | 2017年 / 397卷

关键词：

State estimator; Random parameter matrix; Stochastic nonlinearity; Fading measurement; Correlated noise; Innovation analysis approach; MULTIPLE PACKET DROPOUTS; DISCRETE-TIME-SYSTEMS; RANDOM TRANSMISSION DELAYS; NETWORKED CONTROL-SYSTEMS; RECURSIVE ESTIMATION; LINEAR-ESTIMATION; DESIGN;

D O I：

10.1016/j.ins.2017.02.048

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Using the innovation analysis approach, the optimal linear state estimators, including the filter, predictor and smoother, in the linear minimum variance (LMV) sense are presented for a class of nonlinear discrete-time stochastic uncertain systems with fading measurements and correlated noises. Stochastic uncertainties of parameter matrices are depicted by correlated multiplicative noises. Stochastic nonlinearities are characterized by a known conditional mean and covariance. Different sensor channels have different fading measurement rates. The process and measurement noises are finite-step auto- and/or cross correlated with each other. Two simulation examples verify the effectiveness of the proposed algorithms. (C) 2017 Elsevier Inc. All rights reserved.

引用

页码：118 / 136

页数：19

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