Linear filters for discrete systems with uncertain measurements

Author

Sheng, Mei ; Zou, Yun ; Xu, Shengyuan

Author_Institution

Dept. of Autom., Nanjing Univ. of Sci. & Technol., China

Volume

1

fYear

2004

fDate

6-9 Dec. 2004

Firstpage

52

Abstract

This paper considers the problem of linear filtering with least mean-square errors by using covariance information in linear discrete-time stochastic systems with uncertain measurements. The state and observation noises are correlated noises and the state noises are not white. Recursive algorithms are proposed by employing the orthogonal projection lemma. The features of the designed filter are discussed. Simulation example illustrates the effectiveness of the algorithms.

Keywords

discrete systems; filtering theory; least mean squares methods; linear systems; recursive estimation; stochastic systems; uncertain systems; covariance information; discrete systems; least mean-square errors; linear discrete-time stochastic systems; linear filters; orthogonal projection lemma; recursive algorithms; uncertain measurements; Algorithm design and analysis; Automation; Covariance matrix; Genetic expression; Maximum likelihood detection; Measurement uncertainty; Nonlinear filters; Random variables; Recursive estimation; Stochastic systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Control, Automation, Robotics and Vision Conference, 2004. ICARCV 2004 8th

Print_ISBN

0-7803-8653-1

Type

conf

DOI

10.1109/ICARCV.2004.1468797

Filename

1468797