State estimation algorithms for linear systems with strong disturbance

This paper considers state estimation algorithms for linear systems contaminated by complex strong disturbance. The strong disturbance is characterized as a general random matrix, which can be non-diagonal, and its components (the parameter of each observation channel) can be simultaneously correcte...

Full description

Saved in:
Bibliographic Details
Published inChinese Control Conference pp. 3179 - 3184
Main Authors Pu, Shi, Yu, Xingkai, Li, Jianxun
Format Conference Proceeding
LanguageEnglish
Published Technical Committee on Control Theory, Chinese Association of Automation 26.07.2021
Subjects
Additive noise
Correlation
Deconvolution
Filtering
linear system
Linear systems
Simulation
Smoothing methods
State estimation
strong disturbance
Online AccessGet full text
ISSN1934-1768
DOI10.23919/CCC52363.2021.9549989

Cover

More Information
Summary:This paper considers state estimation algorithms for linear systems contaminated by complex strong disturbance. The strong disturbance is characterized as a general random matrix, which can be non-diagonal, and its components (the parameter of each observation channel) can be simultaneously corrected. For this strong disturbance, we propose an optimal filtering algorithm to filter it in order to estimate the state by using projection theorem. We also present an optimal fixed-interval smoothing and a deconvolution algorithm to address it. Simulation results verify the presented algorithms.
ISSN:1934-1768
DOI:10.23919/CCC52363.2021.9549989