IRUS Total

Noise Covariance Identification for Time-varying and Nonlinear Systems

File Description SizeFormat 
paper.pdfAccepted version1.1 MBAdobe PDFView/Open
Title: Noise Covariance Identification for Time-varying and Nonlinear Systems
Authors: Ge, M
Kerrigan, EC
Item 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.
Issue Date: 22-Sep-2016
Date of Acceptance: 18-Aug-2016
URI: http://hdl.handle.net/10044/1/40609
DOI: https://dx.doi.org/10.1080/00207179.2016.1228123
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
Keywords: 0102 Applied Mathematics
0906 Electrical And Electronic Engineering
Industrial Engineering & Automation
Publication Status: Published
Appears in Collections:Electrical and Electronic Engineering
Faculty of Engineering