Noise Covariance Identification for Time-varying and Nonlinear Systems
File(s)
Accepted version
Author(s)
Ge, M
Kerrigan, EC
Type
Journal Article
Abstract
Kalman-based state estimators assume a priori knowledge of the covariance matrices of the process and observation noise. However, in most practical situations, noise statistics and initial conditions are often unknown and need to be estimated from measurement data. This paper presents an auto-covariance least-squares-based algorithm for noise and initial state error covariance estimation of large-scale linear time-varying (LTV) and nonlinear systems. Compared to existing auto-covariance least-squares based-algorithms, our method does not involve any approximations for LTV systems, has fewer parameters to determine and is more memory/computationally efficient for large-scale systems. For nonlinear systems, our algorithm uses full information estimation/moving horizon estimation instead of the extended Kalman filter, so that the stability and accuracy of noise covariance estimation for nonlinear systems can be guaranteed or improved, respectively.
Date Issued
2016-09-22
Date Acceptance
2016-08-18
Citation
International Journal of Control, 2016, 90 (9), pp.1903-1915
ISSN
1366-5820
Publisher
Taylor & Francis
Start Page
1903
End Page
1915
Journal / Book Title
International Journal of Control
Volume
90
Issue
9
Copyright Statement
This is an Accepted Manuscript of an article published by Taylor & Francis Group in International Journal of Control on 22 Sept 2016, available online at: http://www.tandfonline.com/10.1080/00207179.2016.1228123
Subjects
0102 Applied Mathematics
0906 Electrical And Electronic Engineering
Industrial Engineering & Automation
Publication Status
Published